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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC -

Sectoral Modelling and Analysis

JUNE 2020

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

© 2020 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org

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Cover design: Camille Robinson

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

ACKNOWLEDGMENTS: This Report was prepared by a team of experts from the World Bank and Vivid Economics, with significant contribution from the Ministry of Economic Growth and Job Creation of Jamaica. The Ministry of Economic Growth and Job Creation core team was led by UnaMay Gordon and included Omar Alcock, Le-Anne Roper and Katherine Blackman. The Vivid Economics team was led by John Ward, and also included Benjamin Rizzo and Josh Cowley. Maja Murisic (World Bank) provided inputs and managed the technical assistance project under which this report was prepared. The team wishes to sincerely thank the staff and experts from the various ministries, departments and agencies of the Government of Jamaica who shared their practical insights through meetings, interviews and review of this Report.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Contents

Executive Summary ...... 8 Glossary ...... 11 1 Introduction ...... 13 2 Energy sector ...... 15 3 Land-use change & forestry (LUCF) ...... 30 4 Waste sector ...... 40 5 Agriculture sector ...... 45 6 References ...... 51 7 Annex: Energy model technical description ...... 56 8 Annex: Energy sector commitments...... 76 9 Annex: LUCF sector - additional analysis and comparisons with the TNC ...... 81

List of tables

Table 1 Thirteen energy commitments were identified and modelled in this report ...... 16 Table 2 Electricity generation from renewable sources sees significant growth by 2030 (TWh) ...... 23 Table 3 Jamaica has introduced several policies and actions in the waste sector ...... 40 Table 4 Jamaica has introduced several policies and strategies in the agriculture sector ...... 46 Table 5 Main socioeconomic variables updated in energy model ...... 58 Table 6 Key economic variables updated in the energy model ...... 59 Table 7 Main industry variables updated in the energy model ...... 60 Table 8 Main transport variables updated in the energy model ...... 60 Table 9 Main updates to commerce and services data ...... 61 Table 10 Main updates to residential sector data ...... 62 Table 11 Main updates to energy fuel consumption data ...... 63 Table 12 Main updates to electricity consumption data ...... 63 Table 13 Main updates to electricity production, transformation and distribution data ...... 64 Table 14 Main updates to raw resources price data ...... 65 Table 15 Modelling parameters for Net Billing Mitigation Action ...... 66 Table 16 Modelling parameters for reduction in T&D losses ...... 67 Table 17 Modelling parameters for switch to LNG in Alpart refinery ...... 67 Table 18 Modelling parameters for shift to T8 lighting ...... 68 Table 19 Modelling parameters for shift to smart LED street lighting ...... 69 Table 20 Modelling parameters for LNG buses ...... 70 Table 21 Modelling parameters for B5 blending ...... 70 Table 22 Modelling parameters for implementation of CHP in alumina refineries ...... 71 Table 23 Modelling parameters for reduction in water loss in Kingston...... 72 Table 24 Modelling parameters for energy efficiency within EMEP ...... 73

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Table 25 Modelling parameters for the KMA Urban Traffic Management System ...... 73 Table 26 Modelling parameters for the EECP ...... 74 Table 27 Modelling parameters for modelling the water NAMA ...... 74

List of figures

Figure 1 In 2030, unconditional commitments in the energy and land-use sector could reduce emissions by 25.4%, and conditional commitments by 28.5% ...... 9 Figure 2 Jamaica’s original NDC pledges a 7.8% unconditional reduction, and 10% reduction with international support, in GHG emissions ...... 13

Figure 3 CO2 emissions in the energy sector have fallen since 2006 ...... 15 Figure 4 Energy sector emissions in 2014 were lower compared with the 1990s and 2000s ...... 16 Figure 5 Jamaica’s baseline energy sector emissions are expected to increase by 8% over the period of 2019-2030 ...... 20 Figure 6 The NDCs scenarios achieve between an 18.5% and 21.3% reduction in energy sector emissions ...... 21 Figure 7 Energy emission reductions are led by five key commitments ...... 22 Figure 8 Commerce and services electricity demand reduces with the implementation of energy efficiency commitments ...... 24 Figure 9 Jamaica’s electricity generation output reduces and shifts to low-carbon sources under both NDC scenarios...... 25 Figure 10 Industry’s direct emissions are smaller as a result of the Alpart refinery fuel switch...... 26 Figure 11 The UTMS measure, a conditional NDC commitment, is the main driver of transport emissions reduction ...... 27 Figure 12 Methodology for modelling the LUCF sector ...... 30 Figure 13 Net emissions from LUCF under the baseline scenario ...... 33 Figure 14 Decomposition of net LUCF emissions under the baseline scenario ...... 34 Figure 15 Gradual deforestation in the baseline is offset by afforestation in the NDC scenario ...... 36 Figure 16 LUCF emissions in the NDC scenario are consistently lower than in the baseline ...... 37 Figure 17 No net loss of forest cover drives emissions results in the NDC scenario ...... 38 Figure 18 Agricultural emissions have remained broadly flat since 2007 ...... 45 Figure 19 LEAP model Structure ...... 56 Figure 20. Variable branches of the LEAP model ...... 57 Figure 21 Schematic description of energy policy modelling ...... 58 Figure 22 Land use change and associated emissions are modelled annually to 2030 in our analysis ...... 81 Figure 23 Emissions due to forest removal are significantly greater under our analysis ...... 82

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Executive Summary

This report presents the modelling results that can inform Jamaica’s updated Nationally Determined Contribution (NDC) submission, along with discussing the potential adaptation co-benefits available to Jamaica as a result of its mitigation actions. Jamaica remains committed to its contribution as the world continues to address the challenge of climate change. In line with the voluntary requirements of the Paris Agreement, the country intends to increase the ambition of the mitigation component of its NDC. This increase in ambition comprises both a broadening of the NDC’s sectoral scope and the delivery of greater emission reductions in the sectors that were already covered. The updated NDC submission can cover emissions from land use change and forestry for the first time, as Jamaica looks to broaden the sectoral scope of its NDC. This reflects the importance of the forestry sector to Jamaica, which accounts for more than half of the island’s total land use. It captures the commitments that the country has made to preserve and enhance these stocks. In addition, the updated NDC can reflects a deeper capacity for emissions reductions in the energy sector. Since the original NDC submission, Jamaica has identified further opportunities for emission reductions in the energy sector. These opportunities are part of an increasingly comprehensive approach to decarbonising this sector that covers both electricity generation, as well as all major energy user categories. As a result, Jamaica’s updated NDC could foresee emissions reductions in the land use change and forestry and energy sectors of between 25.4 per cent (unconditional) and 28.5 per cent (conditional) relative to business-as- usual by 2030. This means that, by 2030, emissions in these sectors will be 1.8 to 2.0 MtCO2e lower than they would be in the business-as-usual scenario. 83% of the unconditional reductions comes from the energy sector, as a result of commitments such as the Integrated Resource Plan which will see a large scale ramp up of renewables in the power sector, as well as improved energy efficiency across all major energy consuming sectors. In addition, 17% of these reductions come from the land use change and forestry sector due to the ‘No Net Loss of Forestry’ commitment and the initiative to plant 3 million trees.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 1 In 2030, unconditional commitments in the energy and land-use sector could reduce emissions by 25.4%, and conditional commitments by 28.5%

Source: Vivid Economics

This would represent an increase in absolute ambition from the previous NDC, which targeted a 1.1 to 1.5 Mt CO2e reduction. This reflects both the increased ambition of commitments in the energy sector and the inclusion of mitigation measures in the forestry sector. Under the NDC scenarios, the modelling suggests that Jamaica will virtually decouple economic growth from emissions growth. Over the twelve years between 2018 and 2030, emissions are projected to only grow very slightly within the sectors covered by its NDC, despite expected continued growth of the economy. There are further opportunities to reduce emissions in the agriculture and waste sectors. While the quantified element of the NDC is likely to only focus on the energy and forestry sectors, where the underlying data is most robust, mitigation activities such as the controlled incineration of municipal solid waste capturing and the introduction of sustainable farming practices could also reduce emissions in the waste and agriculture sectors. These activities can provide further benefits for Jamaica and ensure that decarbonisation takes place across the whole economy. The mitigation activities required to meet these targets will also provide adaptation benefits for Jamaica. As a Small Island Developing State, the wellbeing and economic security of Jamaica’s citizens are very likely to be affected by the physical risks of climate change. Adaptation represents an important cross-cutting element for all sectors. Within the energy and land use sectors, the commitments in place to reduce emissions will also provide adaptation co-benefits to Jamaica and therefore enhance the country’s resilience. For example, a shift from heavy fuel oil in electricity production, which requires significant amounts of water for cooling, will reduce the pressure on this scarce resource, while reductions in local air pollution will benefit human health, which will

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

become more important as temperatures increase (Climate Central, 2019). Similarly, the preservation of the forest cover will improve water, soil and air quality, and reduce soil erosion while coastal mangroves provide invaluable protection against storm surges and coastal erosion, particularly during hurricanes. Outside of the sectors covered by the likely quantitative component of the NDC, activities in the agriculture sector will improve food security while waste sectors efforts could reduce leaching and reduce air quality concerns. These modelling results are calculated based on commitments that Jamaica has already made. This ensures that the resulting commitments would be robust, benefiting from both political support and, in many cases, with implementation plans already in place. This approach is crucial in both ensuring the legitimacy of Jamaica’s NDC to domestic constituents and in enhancing the credibility of the international process governing emission reductions.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Glossary

Acronym Definition

API Agricultural Production Index BAU Business as usual BOE Barrel of oil-equivalent CFL Compact fluorescent lamp CHP Combined heat & power CSA Climate Smart Agriculture EECP Energy Efficiency and Conservation Project EMEP Energy Management & Efficiency Programme EU-CIF European Union Caribbean Investment Facility FTE Full-time equivalent GEF Global Environment Facility GNESD Global Network on Energy for Sustainable Development GVA Gross value added HPS High-pressure sodium (lamps) IDB Inter-American Development Bank IMF International Monetary Fund IPCC Intergovernmental Panel on Climate Change IRP Integrated Resource Plan IWM Integrated Waste Management JICA Japan International Cooperation Association JPS Jamaica Public Service JUTC Jamaica Urban Transit Company KMA Kingston Metropolitan Area Ktoe Kilo tonnes of oil equivalent LEAP Long-range Energy Alternatives Planning LEDs Light emitting diodes

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Acronym Definition

LNG Liquid natural gas LPG Liquid petroleum gas LUCA Land Use Change Assessment LUCF Land use change and forestry MEGJC Ministry of Economic Growth and Job Creation MOU Memorandum of understanding MSET Ministry of Science, Energy & Technology MVL Mercury vapour lamps NAMA Nationally Appropriate Mitigation Action NDC Nationally Determined Contribution NEPA National Environment and Planning Agency NSWMA National Solid Waste Management Authority NWC National Water Commission O&M Operation & Maintenance Organisation for Economic Co-operation and Development and the Food and OECD-FAO Agriculture Organization PJ Petajoules PPP Public-private partnership SDGs Sustainable development goals T&D Transmission & distribution TNC Third National Communication UTMS Urban traffic management system WACC Weighted Average Cost of Capital WVO Waste vegetable oil

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

1 Introduction

This report presents the outcome of modelling domestic commitments in Jamaica’s energy and forestry sectors, a qualitative discussion of the waste and agricultural sectors, and a discussion of the adaptation co-benefits the delivery of these commitments might bring, which can be used to inform the update of Jamaica’s NDC.

Jamaica’s original NDC focuses on the energy sector and commits the country to reduce the emissions from this sector by 7.8% by 2030 relative to a business-as-usual (BAU) baseline, rising to 10% by 2030 with international support. Jamaica’s original NDC is expected to lead to energy sector emissions of 13.4 MtCO2e in 2030, or 13.0

MtCO2e with international support, compared to 14.5 MtCO2e in the BAU scenario. By 2025, the country’s original NDC is associated with emissions of 12.4 MtCO2e (or 12.1 MtCO2e with international support), whereas the emissions under its BAU baseline is expected to be 13.4 MtCO2e. This data is summarised in Figure 2 below.

Figure 2 Jamaica’s original NDC pledges a 7.8% unconditional reduction, and 10% reduction with international support, in GHG emissions

Source: Vivid Economics, based on emissions figures from Jamaica’s NDC

The original NDC was designed to be consistent with Jamaica’s National Energy Policy. Specifically, it is based on, at the time of submission, the ‘current level of implementation of the National Energy Policy and the existing pipeline of renewable energy projects’. The additional ambition associated with the conditional commitment is associated with the ‘expansion of energy efficiency initiatives in the electricity and transportation sectors, in line with sector action plans and policies currently under development’ (at the time of writing). This sectoral coverage implies that the NDC covers 94% of CO2 emissions in the economy, but only 12% of its N20 and CH4 emissions.

Jamaica has now committed to expand the ambition of its updated NDC, which it will submit in 2020, as foreseen in the Paris Agreement. This expansion in ambition has two potential dimensions:

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

● To expand the ambition of emission reduction commitments within the energy sector.

● To expand the sectoral coverage of the NDC beyond the energy sector to other sources of emissions, and potential sinks, such as the land use change and forestry sector.

In order to ensure that its NDC is credible and robust, Jamaica intends to only include commitments that are in line with its domestic policy commitments. It is an important policy decision to focus on delivering Jamaica’s commitments, rather than incorporating more speculative policy ideas, as it helps to maintain the integrity of the international process associated with developing and updating NDCs and thus helps ensure the credibility and legitimacy of that process. It is also in keeping with the approach that Jamaica took in its first NDC.

A previous phase of this project identified the relevant opportunities and domestic commitments for inclusion in the modelling exercises presented in this report. The resulting report recommended updating the energy modelling that was undertaken in Jamaica’s Third National Communication, as well as expanding the modelling exercise to include the land use change and forestry sector for the first time. In addition, opportunities in the agriculture and waste sectors were identified as potentially substantial, but due to a lack of data, unlikely to be quantifiable. In this report, the details of these opportunities are instead considered qualitatively.

The impact of commitments in the energy sector was modelled using the Long-range Energy Alternatives Planning (LEAP) energy systems model. This model takes historical data from across Jamaica’s energy system and combines it with projections for growth variables such as GDP and consumption to estimate energy demand and associated emissions. Commitments are defined as changes to inputs and assumptions which lead to reductions in emissions through changes in the energy balance.

To quantify the impacts of commitments in the forestry sector, an Excel-based model was constructed. The model was built using Jamaican data, sourced from reports such as the 2013 Land Use Change Assessment (LUCA) and the National Inventory Report. This local data was combined with international data on carbon sequestration and emissions from sources such as the Intergovernmental Panel on Climate Change (IPCC).

The report presents the energy and forestry modelling approaches and results in turn, before discussing the qualitative analysis of the waste and agricultural sectors.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

2 Energy sector

2.1 Introduction

The energy sector currently makes the largest sectoral contribution to CO2 and total GHG emissions. Jamaica’s

Third National Communication (TNC) shows that the largest contributions to CO2 emissions from within the energy sector are from electricity generation, mining and bauxite, and road transport. Since 2009, CO2 emissions from road transport and electricity generation and have declined slightly: the former fell as a result of lower levels of gasoline consumption; the latter as a result of a reduction in the use of fuel oil and diesel oil for electricity generation as it was displaced by greater wind and solar generation (Francis, 2018). Emissions in mining/bauxite (which includes aluminium manufacture) have been more volatile than in other sub-sectors, notably experiencing a large fall in 2009 due to the global economic downturn.

Figure 3 CO2 emissions in the energy sector have fallen since 2006

Note: Mining/Bauxite includes aluminium manufacture Source: Vivid Economics, based on data from TNC (GoJ, 2018)

Additional data from WRI CAIT, shows that total GHG emissions from the energy sector have fallen since the 1990s, but they may have picked up slightly since 2012. The reduction in emissions since the 1990s has been largely driven by lower emissions from electricity and heat generation. The slight increase in emissions from 2012 appears to have been driven by an increase in emissions from the manufacturing and construction sector.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 4 Energy sector emissions in 2014 were lower compared with the 1990s and 2000s

Source: Vivid Economics, based on data from WRI CAIT

Historically, there have been a number of challenges in reducing energy sector GHG emissions. Jamaica’s power system has low generation efficiency and high losses due to its old equipment and infrastructure. In addition, the regulatory framework in the sector is said to be inadequate to address these issues (GoJ, 2009b). However, efforts have been made to improve this situation, including with the passing of the Electricity Act of 2015. 2.2 Energy commitments This section outlines the energy sector commitments that were analysed in this report. Table 1 describes each of the identified commitments and whether these were assumed to be a part of the unconditional NDC or conditional NDC scenario, with the latter referring to commitments that depend on international finance support. We then provide further detail on the assumptions and data sources used to model these commitments and to calculate their emissions impact (see Annex for details on values used - section 7.4).

Table 1 Thirteen energy commitments were identified and modelled in this report

Commitment Type Description

The Net Billing facility provides a statutory basis that allows self- generators to sell excess energy back to the grid at a pre-agreed level Net billing Unconditional of compensation (GoJ, 2016). By 2020, the Ministry of Science, Energy and Technology targets deployment of 12.8 MW of distributed solar capacity through this policy, up from 0.72 MW installed in 2013.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Commitment Type Description

Targeted This commitment identifies a plan to reduce losses in electricity reduction in Unconditional transmission and distribution by 4.1% of net generation by 2020. T&D losses

LNG in the A switch from fuel oil to LNG as the energy used for process heat Unconditional Alpart Refinery generation at the Alpart refinery in 2020.

Switch to T8 Fluorescent Convert all inefficient 40W T12 lighting to 32W T8 fluorescent lighting Lighting in Unconditional in hospitals and schools between 2020 and 2030. schools and hospitals

Smart LED Upgrade all grid-connected streetlights to LEDs equipped with Unconditional Street lighting pedestrian and traffic sensors during 2017 - 2020.

Introduction of 136 LNG-fuelled Install 136 LNG buses to serve the Montego Bay area and St. James Unconditional public transport Parish in the period between 2020 – 2025. buses by 2025

In 2020, 189 million litres of annual biodiesel production capacity is deployed to satisfy an impending 5%-by-volume biodiesel blending B5 blending Unconditional mandate. Biodiesel is produced domestically from jatropha feedstock commitment and a small amount of waste cooking oil (Ministry of Energy and Mining 2010c).

Use combined heat and power (CHP) technologies to improve energy Improved CHP in Unconditional efficiency in the alumina-refining from 80% to 90% between 2020 and alumina refining 2030.

Reduced water Starting in 2018, water loss in distribution systems operated by NWC is distribution loss Unconditional reduced from 53% to 20% within five years. The measure affects 22% (Kingston) of Jamaica’s water production.

Integrated Jamaica’s IRP document lays out planned deployment of electricity Resource Plan – Unconditional capacity between 2020 and 2038, including 412 MW of solar and wind draft (IRP) capacity between 2020 and 2030.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Commitment Type Description

This commitment reduces the energy consumption of road transport in the Kingston Metropolitan Area (KMA) by 40% between 2017 and EMEP program 2023. This is achieved through installation of devices (optical fibre – Urban Traffic Conditional cables, traffic lights, cameras, sensors, software, communication Management devices, etc.) and the relevant training for implementing an Urban Traffic Management System (UTMS) throughout KMA.

EMEP program Reduces electricity consumption by 30% within public sector health – Energy Conditional and education facilities between 2017 and 2023. efficiency

The Energy Efficiency and Conservation Project (EECP) involved the design and implementation of measures to improve energy efficiency and conservation in the public sector (Williams, 2019). Between 2013 and 2018, the EECP involved the installation of: EECP program Conditional ● over 80,000 sq. ft. of heat reducing film at 37 public sector facilities; ● over 200,000 sq. ft. of cool roof solutions at 11 public facilities; and ● energy efficient air-conditioning solutions at 25 facilities. Consists of two interventions deployed continuously between 2020 and 2030:

● Intervention 1 reduces final electricity intensity in the water NAMA in water services sector by 10% by 2030 due to the deployment of solar Conditional sector PV plants for captive-use generation.

● Intervention 2 includes 55 energy efficiency projects expected to lead to electricity savings of 19,800 MWh per annum by 2030.

Source: Vivid Economics 2.3 Energy modelling results The following section presents the energy modelling results. First, we present a business-as-usual scenario, where commitments identified in Table 1 are not implemented. We then discuss emissions under both the unconditional and conditional NDC scenarios.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

2.3.1 Baseline results We developed a baseline scenario that is used to compare with climate action scenarios. The baseline scenario allowed us to understand the energy use and associated greenhouse gas emissions expected when climate commitments made after 2005 are excluded. We then compared the baseline against climate action scenarios to establish the net impacts of the unconditional and conditional scenarios.

Under projected growth rates of economic activity, and without implementing additional emission reduction commitments, Jamaica’s energy sector emissions are expected to rise as energy demand grows. Based on the IMF projections at the time of writing, Jamaica’s real Gross Domestic Product (GDP) is expected to increase by around 2% per annum between 2019 and 2024.1 We assume this trend to continue from 2024 onwards. GDP growth is expected to be mainly driven by increased domestic consumption and higher industrial activity that result in increased energy demand, and hence increase associated emissions.

Specifically, baseline energy demand is projected to increase by 23% over the period of 2019-2030. Jamaica’s final energy demand of 85 petajoules (PJ) in 2019 is expected to increase to around 104 PJ by 2030. The main drivers of this increase are:

● Increased industrial energy demand: in line with the expected GDP growth, gross value added (GVA) in the Jamaican industry sector is projected to increase by around 2% per year, or 22% over the period of 2019-2030. This increased economic activity leads to increased energy demand of 9 PJ, 27% of current industrial energy demand.

● Increased energy demand in commerce and service: GVA in the commercial sector is also expected to grow by 22% between 2019 and 2030. As a result, energy demand is expected to increase by 9 PJ, or by 70% from 2019 levels, which is largely due to an increased demand for electricity (4 PJ), followed by wood (2 PJ) and LPG (2 PJ).

As a result, baseline energy emissions increase by 8%, from around 7.6 MtCO2e in 2019 to around 8.2 MtCO2e in 2030. This emissions increase is based on increasing energy demand, combined with minimal shifting of energy sources to lower-carbon alternatives and no additional energy efficiency commitments. Figure 5 shows the expected growth in baseline energy emissions in the period of 2016-2030.

1 IMF World Economic Outlook (October 2019)

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 5 Jamaica’s baseline energy sector emissions are expected to increase by 8% over the period of 2019-2030

Source: Vivid Economics

More detail on the data and assumptions in the energy baseline are available in the Annex (section 7.3).

2.3.2 Commitment results The commitments in the unconditional and conditional NDCs result in energy sector emissions reduction of 18.5% and 21.3% relative to the baseline in 2030, respectively. Figure 6 compares the energy sector emissions pathways of both NDC scenarios and the baseline scenario.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 6 The NDCs scenarios achieve between an 18.5% and 21.3% reduction in energy sector emissions

Source: Vivid Economics

Each of the commitments described in section 2.2 lead to emissions reductions in the energy sector, with five commitments driving the emissions reductions. Figure 7 shows the emissions reductions associated with each commitment. The five commitments which contribute the majority of emission savings by 2030 are:

● Integrated Resource Plan: The electricity sector’s emissions are reduced through an expansion of renewable capacity under the Integrated Resource Plan (IRP) as shown further in Table 2.

● Improved CHP in alumina refining: Industrial demand for electricity is reduced by the increased efficiency of CHP technology in alumina refining that reduces emissions by allowing alumina refining to rely less on grid-generated electricity.

● Targeted reduction in T&D losses: Reduced electricity transmission and distribution losses (T&D) decreases the required generation to meet demand, and hence the emissions associated with this generation.

● LNG in the Alpart Refinery: Industrial emissions are further reduced by the switch from residual fuel oil to LNG in the Alpart refinery.

● Urban Traffic Management: The introduction of the Urban Traffic Management System in Kingston drives the reduction of transport emissions. However, as this project is only proceeding with the support of the IDB, JICA, and the EU-CIF, and therefore is only included in the conditional NDC.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 7 Energy emission reductions are led by five key commitments

Note: Conditional commitments are highlighted in red. Because the commitments interact with each other, adding the abatement of each commitment individually does not equal to the combined abatement in the overall NDC scenarios. Source: Vivid Economics

At a sectoral level, the largest emission reductions come from the electricity sector, which accounts for 73% of total abatement in 2030 in the unconditional NDC, followed by the industry and transport sectors. In 2030, the unconditional and conditional NDC commitment packages reduce emissions across the key sectors as follows:

● Electricity emissions are reduced by 42% and 44% in the respective scenarios compared to the baseline in 2030. This translates to around 1.2-1.3 MtCO2e reduction in these emissions.

● Direct industry emissions2 fall by 9% compared to the baseline in both scenarios, or by around 244 ktCO2e in 2030.

● Transport emissions reduce by 2% and 11%, or by 30-220 ktCO2e in respective scenarios. The difference between scenarios is due to the EMEP traffic management commitment that is only implemented in the conditional NDC scenario.

The large reduction in electricity emissions is partly driven by a substantial change in electricity generation technology, with a pronounced shift towards renewable technologies. The IRP aims to significantly increase deployment of solar, onshore wind, and hydroelectric generation, compared with both current levels and the baseline scenario. For example, solar PV generation in the unconditional NDC scenario in 2030 is eight-times

2 To avoid double counting, only the direct emissions from industry are included here. This includes emissions due to direct combustion of fuels but not those emissions associated with industrial electricity consumption

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

higher than in baseline in the same year. Table 2 presents details on the electricity generation levels in 2018 and 2030 for main renewable generation sources.

Table 2 Electricity generation from renewable sources sees significant growth by 2030 (TWh)

Renewable Current generation Baseline Unconditional Increase from Increase from generation sources (2018) (2030) NDC (2030) 2018 generation 2030 baseline

Onshore wind 302 347 623 106% 79%

Solar PV 46 74 655 1,323% 782%

Hydro 179 145 334 87% 131%

Total 527 566 1,611 270% 192%

Source: Vivid Economics using data from MSET (2019) Jamaica’s total alternative consumption 2018

In addition, by 2030, commitments that reduce electricity demand are expected to decrease electricity generation output by 25% and 26% compared to the baseline under the unconditional and conditional NDC scenarios respectively. The overall 2030 reduction in electricity generation is due to a combined impact of reduced electricity demand and reduced T&D losses:

● Reduced electricity demand: commitments drive an 18-19% reduction in electricity demand by 2030, or 0.9-1.0 TWh, in the respective NDC scenarios.

 This reduction is largely a result of a gradual decrease in industrial demand for grid-generated electricity as more efficient CHP technology is deployed in the alumina-refining sector, allowing refineries to rely less on grid-based electricity than under the baseline. This initiative accounts for 85- 91% of the total electricity demand reduction. In isolation this commitment reduces electricity generation by 0.9 TWh compared to the baseline in 2030.

 By 2030, commerce and services show a small reduction in electricity use of 0.1-0.2 TWh compared to the baseline in the respective scenarios, or 3-6%. This is as a result of commitments to switch from HPS and MLV street light bulbs to more energy efficient LED lamps; reduced water distribution losses resulting in lower electricity demand in Kingston; a switch from T12 to more energy efficient T8 light bulbs in hospitals and schools. In the conditional NDC scenario, the water sector NAMA further reduce electricity demand from this sector, while the EECP and EMEP – Energy efficiency programs reduce electricity use in public sector (Figure 8).

● Reduced electricity T&D losses: In isolation, this commitment is expected to cause a reduction in electricity generation output of around 0.6 TWh in 2030 relative to baseline.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 8 Commerce and services electricity demand reduces with the implementation of energy efficiency commitments

Note: Street lighting: the net reduction is a result of switching from HPS and MLV light bulbs (-0.07 TWh) to more energy efficient LED lamps, increasing their electricity demand (-0.02 TWh). Water services: reduced water distribution losses result in reduced electricity demand in Kingston (-0.03 TWh) with additional impact of NAMA interventions under the Conditional NDC (-0.04 TWh). Efficient lighting and appliances savings reflect the switch from T12 to more energy efficient T8 light bulbs in hospitals and schools. All other reflects EECP and EMEP – Energy efficiency programs’ reduction of electricity use in public sector. Source: Vivid Economics

The combined impact is that the output of the electricity generation is significantly lower than it otherwise would be, with gas-fired power generation absorbing most of the reduction. While gas-fired power generation grows year-on-year in the baseline scenario, reaching 6.9 TWh of electricity output in 2030, in the NDC scenarios only around 4 TWh of electricity is expected from this technology in 2030. Figure 9 summarises electricity generation output by key electricity sources for each scenario.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 9 Jamaica’s electricity generation output reduces and shifts to low-carbon sources under both NDC scenarios

Source: Vivid Economics

The reduction in direct industrial GHG emissions is dominated by Alpart refinery’s fuel switch from residual fuel oil to LNG. Following its closure for modernisation, the analysis assumes the Alpart refinery switches from residual fuel oil to LNG. Accounting for growth in alumina demand, this commitment reduces the demand for residual fuel oil by 1.9 million BOE by 2030 compared to the baseline. Given that LNG is less carbon intensive than residual fuel oil, the fuel switch results in net emission savings of around 240 ktCO2e by 2030 relative to the baseline as shown in Figure 10.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 10 Industry’s direct emissions are smaller as a result of the Alpart refinery fuel switch

Note: Other changes reflect emission savings associated with reduced use of diesel in bauxite mining (-3 ktCO2e), construction (-3 ktCO2e) and other manufacturing (-5 ktCO2e). Source: Vivid Economics

Finally, in the conditional NDC, the introduction of the UTMS in Kingston drives the emission reductions in the transport sector. The NDC commitments result in the following impact across different modes of transport (Figure 11):

● Unconditional commitments: This scenario reflects the impact of B5 blending commitment from 2020 onwards which reduces transport emissions by around 28 ktCO2e in 2030. The introduction of more energy efficient LNG-fuelled buses is expected to decrease GHG emissions by 5 ktCO2e by 2030.

● Conditional commitments: The UTMS aims to reduce transport fuel consumption in the Kingston Metropolitan Area by 40% relative to the baseline between 2017 and 2023. This is estimated to reduce road transport’s fuel consumption of gasoline and diesel by around 470 thousand BOEs by 2030. Overall, this is expected to drive 190 ktCO2e of emission reductions in the transport sector in the conditional scenario, out of a total of 220 ktCO2e.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 11 The UTMS measure, a conditional NDC commitment, is the main driver of transport emissions reduction

Note: ‘Other’ includes reduction in buses’ emissions due to switching to LNG (-5 ktCO2e) and reduced emissions from tractors due to B5 blending (-1 ktCO2e). In the case of the conditional NDC, the ‘Other’ category includes reduced emissions of motorcycles and tractors due to the UTMS commitment Source: Vivid Economics

2.4 Adaptation and other co-benefits In addition to their climate change mitigation benefits, Jamaican energy commitments will deliver multiple further co-benefits at the individual, sectoral, national, and international levels. These benefits will derive from both the energy and water efficiency measures as well as the increased deployment of low-carbon and renewable energy sources. Key climate change mitigation co-benefits can be categorised to broadly four groups (adapted based on Mayrhofer & Gupta, 2016):

● Climate-related: enhanced resilience to climate change or enhanced adaptation to climate change.

● Economic: strengthened energy security; increased private investment; improved economic performance; positive employment impacts; technological change.

● Environmental: reduced pressure on finite resources and on wider ecosystems.

● Social: reduced air pollution impacts; enhanced energy access; improved water security; better human health; reduced traffic congestion.

Climate change is expected to increase pressures on Jamaican’s water sector and its supply of freshwater across different uses. Jamaica’s water supply has been under a strain for a combination of external factors including droughts, shifting patters of rainfall, population growth and urbanisation (Inter-American Development Bank,

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2015). In 2014, Jamaica experienced water stress3 of 11%, slightly below the world average of 13% (FAO, 2018). Climate change will likely exacerbate these pressures by increasing the length of dry seasons; increasing the frequency of very intense rains; and decreasing rainfall and increasing temperatures (CSGM, 2017).

Jamaica’s commitments to reduce water distribution losses, shift away from resource-intensive combustion fuels, and increase energy efficiency will all help reduce these pressures. Reducing water distribution loses in Kingston will directly reduce water supply needs. Further, the shift from heavy fuel oil power generation to renewables and more efficient natural gas power plants, will further reduce pressure on water resources because of their lower water consumption requirements (Spang et al., 2014).4 In addition, improved energy efficiency will reduce demand for electricity generation and the associated use of water in electricity generation (Fricko et al. 2016).

Jamaica’s energy commitments are also expected to deliver a wide set of economic benefits:

● Energy security: With its current high dependency on fossil fuel imports (93% in 2018), Jamaica’s mitigation policy measures will help to reduce its reliability on imports by both reducing overall energy needs and facilitating a shift to local renewable generation sources (GoJ, 2019). This will allow Jamaica to increase the resilience of its energy supply and reduce its vulnerability to fossil fuel price fluctuations (GNESD, 2010).

● Economic efficiency and international competitiveness: The cost of using petroleum for electricity production is several times higher than alternative fuels and burdens Jamaica’s global competitiveness (IMF, 2016). Jamaica’s electricity system also suffers from large transmission and distribution losses (29.5%, third highest among Caribbean countries). The planned energy efficiency measures, such as further reduction in electricity’s transportation and distribution losses and increased use of CHP generation in alumina refineries, can result in increased economic output per unit of energy input and thus enhance international competitiveness and provide wider economic benefits. Previous modelling analysis suggests that meeting Jamaica’s energy efficiency targets and renewable energy targets could lead to a reduction in the national electricity bill of 31% and 4%, respectively, leading to an increase in the long-term GDP level of 14% and reduced inflationary pressures (IMF, 2016).5

● Positive employment impact: Jamaica’s increased deployment of renewables and energy and water efficiency measures can also have a positive impact on its employment levels in the medium and long- term. For instance, a global modelling study estimated that, for every million USD spent, renewables and energy efficiency could create between 7.5 and 7.7 full-time equivalent (FTE) jobs, respectively, compared to 2.7 FTE jobs in the fossil fuel sector (Garrett-Peltier, 2017). A recent modelling of Jamaica’s full transition to renewables and energy efficiency under a scenario estimates that this could create almost 23,000 long-term, full-time net jobs (Jacobson et al., 2019). This accounts for construction and

3 Water stress is a measure of the pressure that human activities exert on natural freshwater resources. It is defined as the proportion of water withdrawal by all sectors in relation to the available water resources. 4 Gas/oil steam turbine plants’, with a cooling tower, water consumption is estimated to be 0.768 m3/GJ. More efficient gas/oil plants using combined cycle consume 0.221 m3/GJ, solar PV panels use 0.006 m3/GJ, and wind power’s water consumption is very marginal (less than 0.0005 m3/GJ). 5 IMF (2016) analysis assumes that Jamaica will reduce its energy intensity from 22 million to 6.3 million joules per USD of GDP, supply 20% of energy from renewable sources and generate 20% renewable electricity by 2030, as presented in the 2014 Caribbean Sustainable Energy Roadmap and Strategy.

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operation jobs in renewable and energy efficiency sectors as well as job losses due to reduced mining, transporting, processing, and fossil fuels’ energy use (Jacobson et al., 2019).

Jamaica’s mitigation commitments will also reduce national and global environmental pressure beyond those associated with climate change. The evidence on these benefits tends to only be available at the global level (based on Luderer et al. 2019). Latest available estimates of sulphur and nitrous oxides (monitored between 2011 and 2013) suggest that emissions were below national standards, due in part to low-sulphur fuels and the “polluter pays” principle that applies to industries (NEPA, 2015). Nonetheless, it is generally recognized that reduction in the use of fossil fuels can reduce terrestrial acidification (European Environment Agency, 2016). It can also reduce potential ecotoxicity (Luderer et al., 2019).

They can also deliver several significant social benefits. Reduced air pollution can result in lower numbers of persons likely to develop respiratory and cardiovascular diseases. If carefully designed together with air pollution mitigation measures, Jamaica’s energy commitments could significantly decrease energy-related air pollution emissions and pollution exposure (IEA, 2016). Jacobson et al. (2019) estimates that by 2050, Jamaica could save lives from air pollution, and reduce associated annual social health costs by $3 billion (Jacobson et al., 2019). As it relates to reduced traffic congestion, a 2018 survey of car commuters in the Kingston area found that 93% of respondents indicated an increase in employee lateness due to traffic congestion (HRMAJ, 2018). Traffic congestion can result in substantial social costs including the loss of productive time and reduced quality of life; increased fuel consumption, vehicle operation and maintenance costs; and increased accident risks, and air pollution. For example, a traffic analysis of Molynes Road in Kingston estimated a loss of productive time amounting to US$163 to US$732 lost on average, per working person over a three-year period (Selby and Romanel, 2019).6

6 This range is due to assumed maximum free-flow speed of 60km/hour and 100km/hour with no congestion, resulting in different amounts of time lost and thus different productive time losses due to traffic congestion.

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3 Land-use change & forestry (LUCF)

3.1 Introduction Although it was not included in Jamaica’s previous NDC, the LUCF sector is of vital significance to Jamaica’s climate change response. 40% of Jamaica is densely forested, associated with 1.3 MtCO2 of sequestration in 2013. Despite the country’s ambitious economic development plans, Jamaica is committed to retaining its extensive forest cover. This will both support its low-carbon growth aspirations and deliver a wide range of adaptation benefits.

Building on stakeholder discussion, there are two key commitments that can be quantified and potentially included in the next NDC:

● The commitment to ‘no net loss of forest cover’, which aims to maintain the level of forest cover identified in the 2013 Land Use Change Assessment (LUCA) by 2026’.

● The commitment to plant 3 million trees over the period 2019-2022. 3.2 Model description The analysis draws on a spreadsheet model that quantifies the emissions impact of these commitments. The model establishes both a baseline and an NDC scenario for forest coverage drawing on both historical data and Jamaica’s climate commitments. The model combines this with international data on the carbon density of Jamaica’s existing forests and sequestration rates for newly forested land to estimate the projected impact of these commitments on sectoral emissions. Figure 12 sets out the modelling process.

Figure 12 Methodology for modelling the LUCF sector

Note: Driving variables include macroeconomic forecasts and agricultural production targets. Source: Vivid Economics

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3.3 Baseline scenario The baseline uses data on historical trends in forest cover and domestic targets for land use. The calculation of baseline emissions combines the methodology that was used in Jamaica’s Third National Communication (TNC) with data on Jamaican forest cover by type from the 2013 Land Use Change Assessment (LUCA) to calculate emissions in the period to 2013. For emissions to 2030, this methodology is expanded based on future changes in land use demand. Specifically, the baseline calculations involve the following steps:

1. Combining the updated land use statistics from the 2013 LUCA with emissions and sequestration factors from the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions figures for 2013.

2. Drawing on historical trends and an analysis of changes in forest cover to estimate baseline plantings and removals. This provides a baseline forecast for forested land to 2030.

3. Applying sequestration and emissions factors to the changes and levels of forest land to estimate forestry sector emissions.

The baseline provides land-use projections for three broad forest categories:

● Tropical forest as Jamaica’s primary forest type. In the 2013 LUCA, 431,543 ha were identified as tropical forest, excluding mangroves.

● Mangroves, which capture between 3 and 4 times the emissions of other forest types, have strategic adaptation and other benefits distinct to those of tropical forest. The 2013 LUCA identified 9,752 ha of mangroves.

● Mixed land captures less densely populated forest land (either due to degradation or mixed uses). 203,199 ha of land were classified as mixed land in the 2013 LUCA.

These categories are disaggregated into granular land categories to provide more accurate estimations of emissions. Within these categories, the model further disaggregates to account for differing emissions factors and hence generate a more robust projection. The weights used for disaggregation are based on Jamaica’s 2003 National Land Inventory Report, the most recent source available that aligns land use categories as reported in the LUCA with forest types based on emissions and sequestration factors (GoJ, 2004). For example, within tropical forest, there is a further breakdown between natural forest and plantations, rainforest, moist forest, mountain systems and dry forest.

The baseline can be compared with the baseline developed for Jamaica’s Third National Communication. The differences between the two baselines reflect new data and a more sophisticated methodology for developing the projections between 2013 and 2030.7 In order to provide consistency within Jamaica’s climate policy framework, section 9 provides more details on these differences, building on dialogue with key Jamaican stakeholders.

7 The TNC assumed constant emissions from the forestry sector between 2012 and 2030.

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3.4 NDC scenario The NDC scenario projects a forest cover pathway consistent with commitments. This scenario requires defining how the commitments of “No net loss of forest cover” and planting 3 million trees in 2019-2022 will be implemented, as this influences emissions drivers such as the ratio of afforestation to deforestation, the different forest/tree type, and different plantation management techniques. Once the commitments are defined within these dimensions, it is possible to develop an NDC scenario emissions pathway, and compare this with the baseline.

Building on discussions with policymakers, the key assumptions made for projecting the emissions impact of the no net loss of forest cover commitment are:

● That the commitment is primarily achieved through afforestation, as, under the commitment, forest removals must be offset by replanting forest area of at least an equal size. There is also a smaller reduction in deforestation, as the afforestation requirement increases costs of forest removal, which reduces forest clearing. Based on this, the model assumes that 75% of the change in net deforestation comes from increased afforestation, with the remainder attributed to reduced forest removal.

● The proportions of different forest cover categories planted and protected by the commitment are based on historical removals and additions. This means that under the commitment, all types of forest cover see increased afforestation and reduced deforestation in line with historical trends in their additions and removals taken from the 2013 LUCA. As a result, the forest types with the highest levels of addition and removal in the LUCA, dry and mountain forests, are the types modelled to be most influenced by the no net loss commitment.

● Net deforestation is reduced by the same amount each year from 2013 to 2030. Relative to the baseline, the commitment reduces net deforestation by an equal amount each year between 2013 and 2030. In practice, this ensures that forest cover is held at 2013 levels throughout the modelling exercise, due to the constant rate of deforestation under the baseline. The model also assumes the commitment is extended beyond 2026 as currently legislated, based on discussions with the Forestry Department.

Building on discussions with policymakers, the key assumptions made for projecting the emissions impact of the three million trees initiative are:

● Only seedlings planted centrally are included in afforestation figures. The commitment includes 1 million saplings distributed to individuals, in addition to 2 million planted centrally as forest cover. We assume that distributed saplings are planted in urban and non-forest locations, and therefore increases in forest cover are based only on the 2 million trees planted as forest cover.

● Forest density is 667 trees per hectare. This comes from Jamaica’s Forestry Department, which estimates that 2 million trees will add 3,000 hectares of forest cover (Forestry Department, 2019). This figure is in line with international estimates (Dombro, 2010).

● Plantings are equally distributed between 2020 and 2022. Applying a constant planting rate between 2020 and 2022 gives a smoother projection for land use and emissions. In addition, given low levels of planting at the end of 2019 this simplifying assumption does not have a material impact on results.

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3.5 LUCF modelling results This section provides the results for the baseline and NDC scenario in turn.

3.5.1 Baseline results Under the baseline, net emissions from LUCF increase by 2030, driven by the growth in land needed for agricultural production. Increasing demand for agricultural land leads to a reduction in forest cover by 2030. As a result, net emissions are higher by 2030 than in 2013 as shown in Figure 13.

Figure 13 Net emissions from LUCF under the baseline scenario

Source: Vivid Economics

The increase in net emissions is driven by a reduction in sequestration from existing forest cover. Emissions due to forest removal are constant between 2013 and 2030. However, annual sequestration from existing forest land is slightly lower in 2030, due to the deforestation between 2013 and 2030. This reduction in forest cover leads to the increase in net emissions by 2030.

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Figure 14 Decomposition of net LUCF emissions under the baseline scenario

Source: Vivid Economics

Deforestation rises in the baseline due to the increase in agricultural land implied by agricultural production targets. Jamaica’s Vision 2030 document targets a significant increase in the Agricultural Production Index (API), which implies an increase in land devoted to agriculture. As a result, the baseline foresees steady deforestation over the period. More detail on agriculture as a driver of deforestation is available in Box 1.

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Box 1 Agricultural production as a driver of deforestation

The removal of forest cover for agricultural land is a key driver of forest cover in Jamaica (Winrock International, 2018). An analysis of the drivers of deforestation in Jamaica found that over 80% of deforestation in the 1998-2013 period was due to subsistence and commercial agriculture, with most of the remainder attributable to infrastructure.

Jamaica’s Vision 2030 targets a sustained increase in agricultural production to 2030, in contrast with historical trends. Over the 1998-2013 period, Jamaica’s agricultural production index fell by around 10%, driven by difficult natural conditions and increasing demand for land for other purposes. However, the Vision 2030 document targets an increase in agricultural production of 40% over 2013 levels.

As a result, even allowing for agricultural land productivity improvements, the baseline scenario features less forest cover than in the recent past. In the 2013 LUCA, cropland removals were around 8,000 hectares per year between 1998 and 2013. However, in order to meet agricultural production targets, our analysis suggests that cropland will need to expand slightly during the 2013-2030 period. This leads to gradual levels of deforestation in the baseline scenario. The table below outlines the key variables and assumptions associated with agricultural land and production.

Year Cropland (ha) Agricultural Production Index Productivity of land (API / kha)

1998 356,805 131 0.37

2013 223,896 119 0.53

2030 245,439 (implied) 176 (targeted) 0.72

Period Net change in cropland Change in Agricultural Annual growth (ha/yr.) Production Index

1998- -7,876 -12 0.01 2013

2013- 1,457 (implied) 57 0.01 (assumed 2030 constant)

Note: 2030 values are based on Agricultural Production Index targets and historical changes in land use and land productivity. Source: JAMSTATS; 2013 LUCA; Vivid Economics analysis

A comparison of the baseline results with TNC trajectories is presented in the annex (section 9).

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3.5.2 NDC results Under the NDC scenario, forest cover increases over the period to 2030. This contrasts with gradual deforestation under the baseline. The profile of forest cover in the NDC scenario is driven by the no net loss of forest cover target, which requires a combination of afforestation and a small reduction in deforestation relative to the baseline. In addition, the commitment to plant 3 million trees increases forest cover by 3,000 hectares during the 2019-2022 period. By 2030, total forest cover is around 20 kilo-hectares higher in the NDC scenario than in the baseline, a difference amounting to almost 2% of total Jamaican land.

Figure 15 Gradual deforestation in the baseline is offset by afforestation in the NDC scenario

Source: Vivid Economics

As a result, by 2030, net sequestration in the NDC scenario is 30% higher than in the baseline, around 0.3

MtCO2. The 18 kilo-hectares of additional forest cover in 2030 leads to an additional 0.2 MtCO2 sequestration from forest cover. In addition, the lower levels of deforestation in 2030 reduce emissions by 0.1 MtCO2 compared with the baseline. The overall impact on net emissions is 0.3 MtCO2.

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Figure 16 LUCF emissions in the NDC scenario are consistently lower than in the baseline

Source: Vivid Economics

The commitment to no net loss of forest is the primary driver of the results. Achieving no net loss of forest cover leads to increased afforestation and a slight reduction in gross deforestation each year to 2030. The 3 million trees initiative also increases afforestation during the years it is active, leading to further sequestration in those, and subsequent, years. By 2030, 85% of the additional sequestration under the NDC scenario is the result of the no net loss commitment, with the remainder coming from the 3 million trees initiative.

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Figure 17 No net loss of forest cover drives emissions results in the NDC scenario

Source: Vivid Economics

3.6 Adaptation and other co-benefits In addition to their mitigation benefits, Jamaican reforestation and afforestation initiatives can improve the country’s resilience to climate shocks and trends by providing a range of ecosystem services. Maintaining and increasing the level of forest cover in Jamaica can provide co-benefits for the country’s infrastructure resilience, health outcomes, water resources, and coastal ecosystems:

● Mitigation actions in the forestry sector can improve the resilience of infrastructure. Reforestation and afforestation can reinforce the soil, reducing susceptibility to landslides and flooding, providing infrastructure protection. In addition, actions to restore and replant mangroves provide additional coastal protection against storms and floods, where most of the country’s infrastructure is situated.

● There are several mechanisms through which beneficial health outcomes can be achieved. First, reforestation and afforestation actions can help reduce localised heat stress. Further, sustainable forest management decreases soil erosion and water runoff, with reduction in the occurrence and/or impacts of landslides, flooding, and storms – and hence the damaging health impacts these events can cause.

● Mitigation actions in the forestry sector can improve the management of water resources. Ensuring sustainable and integrated land use, including in watershed areas, can help maintain optimal levels of groundwater, increase water availability, and improve its quality. In addition, restored mangroves provide water filtration services, thereby improving overall water quality and counteracting the effect of saltwater intrusion.

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● The sector’s mitigation actions can help improve the performance of the agricultural sector. Improved maintenance of watersheds and expansion of agroforestry practices can help reduce soil erosion, improving the productivity of land and crop yields in the context of worsening agricultural conditions. Initiatives to promote sustainable livelihood options, particularly in rainforest buffer areas, to take off the pressure on forest exploitation (also taking into account gender balance), as well as decreasing deforestation risks, also improve food security and agricultural resilience.

● Coastal and terrestrial ecosystems can also benefit. Restoring mangroves in coastal regions improves biodiversity and ecosystem services and provides protection for coastal ecosystems. Mangroves also provide nursery habitats for many marine species and contribute to sustaining the abundance of local fish stocks and shellfish populations. Whilst the no net loss of forest cover target does not specify the types of forest cover to be replanted, adaptation benefits associated with mangroves should be considered alongside their high carbon sequestration rates. Terrestrial forests have significant biodiversity which would be enhanced through improved forest management.

The concentration of co-benefits associated with mangroves suggests these forests may be of strategic importance to Jamaica. While the no net loss of forest cover commitment does not specify the types of forest cover that will be protected or replanted, these co-benefits may provide additional reasons to focus support on these ecosystems, in addition to their high sequestration capabilities.

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4 Waste sector

Jamaica recognises waste management as a critical issue and has in place policies to reduce environmental and societal pressures. Currently, the country generates an estimated 1kg/per person/per day of solid waste (Kaza et al., 2018; PIOJ, 2019). The island’s four main wastesheds recorded about 744,280 tonnes of waste (PIOJ, 2019). Most waste is disposed of in managed dumpsites and the availability of land to deal with increasing volumes of waste is of national importance, especially as Riverton, the country’s largest disposal site, reached its nominal maximum capacity by 2014. 69% of the solid waste generated in Jamaica is organic, representing a potentially good source of input into an energy‐from‐waste sector. This is recognized by the National Energy from Waste Policy for 2010-2030.

The waste management is also gaining attention from citizens. There are widespread calls to improve the current solid waste management infrastructure, especially after recent landfill fires have raised the profile of the negative health impacts associated with the current approaches to the management of municipal solid waste. 4.1 Emissions trend The overall emissions trend in the waste sector is stable, and accounts for around 5% of net GHG emissions.

However, the solid waste management sub-sector makes the largest contribution, and emissions from this sub- sector are growing. The sub-sector alone contributes to 3% of net GHG emissions, primarily methane (CH4). These emissions, primarily from landfills, show a steady increase over time and were 16% higher in 2012 than 2006. 4.2 Policy framework and specific actions Jamaica has introduced several important policies, plans, and projects, particularly with respect to the role of energy from waste infrastructure, to reduce the impact of waste on climate, wider environment and citizens. These are summarised in Table 3.

Table 3 Jamaica has introduced several policies and actions in the waste sector

Overarching policy frameworks Projects and other specific actions

• Climate Change Policy Framework • Plastic Waste Minimisation Project (ongoing) for Jamaica (2015) • Tyre incineration to energy for cement production (2019) • National Energy from Waste Policy • PPP for Integrated Waste Management (in progress) 2010-2030 (draft) • Consultations to update solid waste management plan • Single use plastics ban

Source: Vivid Economics

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4.2.1 Policy frameworks Jamaica’s 2015 Climate Change Policy Framework acknowledges the importance of the Draft Energy from Waste Policy 2010-2030, in advancing climate change imperatives. The latter is especially important for exploring the feasibility of landfill gas recovery and utilisation and supporting measures to reduce waste including re-use, recycling, and composting.

Jamaica’s draft National Energy-from-Waste Policy 2010-2030 aims to ensure that: ‘Jamaica is the regional leader providing affordable and clean energy from waste contributing to a sustainable future’. Jamaican’s framework recognises the high importance of waste management and sets four goals for the country:

● Goal 1: Jamaica creates economic infrastructure and planning conducive to the development of the energy‐from‐waste sector

● Goal 2: Jamaica builds its energy‐from‐waste sector on the most appropriate technologies that are environmentally friendly, producing a clean and reliable renewable source of energy

● Goal 3: Jamaica creates partnerships between the energy sector and the waste management and agriculture sectors to facilitate the continuous streams of waste into the energy from waste

● Goal 4: Jamaica has a well‐defined governance, institutional, legal and regulatory framework for the generation of energy from waste

In terms of the energy-from-waste options, the draft National Energy-from-Waste Policy 2010-2030 identifies a number of technologies that could play a pivotal role in the growth of the sector. These include, for example, incineration of municipal solid waste; capture of landfill gas; production of biodiesel from cooking oil; electricity co-generation using bagasse; production of biogas using animal wastes; wastewater sludge treatment.

Further, consultations are underway to update 2001 National Solid Waste Management Plan. One of the potential regulations that may be enacted relate to the segregation of waste at source and to the disposal of polyethylene terephthalate bottles.

Jamaica has also introduced a single use plastics ban. Since the start of 2019, the government has imposed a ban on the importation, manufacture, distribution and use of single use plastic bags (of certain sizes) as well as plastic straws. From January 2019, there has been a ban on the importation of polystyrene foam (‘Styrofoam’) for use in the food and drink industry, while from the start of 2020 the ban will be extended to cover the domestic manufacture of these items as well.

4.2.2 Projects and other specific actions As well as the draft National Energy-from-Waste Policy 2010-2030, there are several projects already in pipeline:

● Biodiesel from cooking oil. While there are some small operations in existence, there is no national system in place for collecting used cooking oil. Jamaica’s pilot project will establish 10 acres of inter‐ cropped jatropha and castor plants at the Bodles Research Station in Old Harbour, St Catherine. The research will consider the harvest potential under prevailing climatic condition and will seek to determine the productivity of feedstock varieties on marginal lands.

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● Production of biogas using animal wastes. The Scientific Research Council has been involved in the development of biogas plants using animal wastes in the agricultural, small manufacturing, educational and residential sectors. A total of 250 plants are in operation across the island with future work needed to ensure full acceptance of biogas as a fuel for cooking.

● Biodigester sector. Jamaica’s Scientific Research Council has also supported a number of small biodigester projects. This is a part of the implementation programme of the National Energy from Waste Policy and should reduce the emissions associated with waste management.

The Waste Enterprise Team is currently developing proposals for a country-wide Integrated Waste Management (IWM) public-private partnership (PPP) concession. The concession will cover multiple sources of waste across the country through a PPP for waste collection, transfer and disposal. It is expected that the focus will be on energy from waste, as the waste will be primarily organic, to improve the footprints of landfills and reduce unmanaged dumping. Other methods, such as recycling, are expected to be favoured for other types of waste, such as metal and plastic. A timeline of 16 months to operation is envisaged.

The Government is partnering with the Caribbean Cement Company to remove between 1.5 and 2.0 million tyres from the Riverton City landfill for incineration and energy generation. The removal and disposal have been formalised with the signing of a memorandum of understanding (MOU). Under the terms of the MOU, the National Solid Waste Management Authority (NSWMA) will supply five truckloads of old tyres per day from the Riverton City disposal site in Kingston for incineration at the Caribbean Cement Company plant in Rockfort in east Kingston. The pilot project was undertaken on 40 non-consecutive days, over a period of three months from August 2019.

Finally, Jamaica has recently rolled out the Plastic Waste Minimisation Project. The project has commenced in February 2018 and is being implemented by the National Environment and Planning Agency with funding for the UN Environmental Programme. J$33 million has been allocated to the Project in 2019/20, launching Rae Town Plastic recycling project to reduce the levels of plastic entering the Kingston Harbour and to provide plastic recycling opportunities for communities. 4.3 Contribution to climate change mitigation While it has not been possible to aggregate the impacts of existing policy commitments at the national level, Jamaica’s policies and activities in the waste sector are likely to further lower the country’s greenhouse gas emissions. This section discusses the channels through which these policy measures could result in reduced greenhouse gas emissions and indicates possible size of the impact based on existing evidence.

Looking at Jamaican’s draft National Energy-from-Waste Policy’s list of proposed energy-from waste technologies, we expect further reduction in GHGs emissions based on the existing evidence:

● Incineration of municipal solid waste technologies: incineration is environmentally preferred to uncontrolled dumping as it reduces the volume of waste by over 85%. Further, incineration and other energy recovery processes avoid most of the GHG emissions that would have been otherwise emitted at

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landfills. Further, energy-from-waste plants will reduce power sector carbon emissions if displacing heavy fuel oils in Jamaica’s electricity generation.8

● Capture of landfill gas. 69% of Jamaican municipal waste stream consists of organic matter, which generates large volumes of methane gas. Instead of escaping into the air, landfill methane gases can be captured, processed, and used as a source of energy In this way, capturing landfill gas for electricity generation reduces both landfill methane emissions and electricity emissions associated with the use of higher carbon content fuels.

● Production of biodiesel from cooking oil. Existing life-cycle studies show a large range of GHG mitigation potential for different biofuels depending, for example, on the production process. A review of 60 studies showed that most conventional biofuels could reduce GHGs emissions by 20-70% when replacing fossil fuels use in transport (IEA, 2011).

● Co‐generation using bagasse. Approximately 160,000 tonnes of bagasse – equivalent to about 245,000 barrels of oil– are used per annum (as of 2018) in cogeneration in Jamaica’s sugar factories (GoJ, 2019a). Using sugarcane bagasse can significantly reduce carbon emissions – a case study from Brazil suggests a 79% reduction in emissions by -79% in a context in which diesel-based electricity generation is replaced (Coelho et al., 2019).

● Production of biogas using animal wastes. Biogas use reduces GHGs emissions as methane in biogas can replace fossil fuels for process heat or electrical generation and avoid emissions associated with untreated animal waste (Bogner et al., 2007). For example, a case study of an intensive dairy farm in Italy estimated GHGs emissions savings due to anaerobic digestion between -23.7% and -36.5% (Battini et al., 2014). Deriving exact mitigation impacts of Jamaican biogas would, however, require further research.

● Use of wastewater sludge: Sludge is rich in nutrients such as nitrogen and phosphorous and contains valuable organic matter. Wastewater control and treatment of methane from anaerobic processes can replace fossil fuels for process heat or electrical generation, reducing overall GHGs emissions (Bogner et al., 2007).

In terms of the Riverton City landfill for incineration and energy generation pilot, the National Environment and

Planning Agency (NEPA) expects that this will result in a small reduction in CO2 emissions. This could reduce emissions by displacing coal as the fuel used for thermal heat generation in cement production.

The Plastic Waste Minimisation Project can also contribute to carbon emissions reductions. Any decrease in the production of waste and increase in recycling should reduce GHGs emissions from waste through a reduction in primary production and associated energy demands. For example, plastic recycling is estimated to save 1,024 kgCO2e for every tonne of recycled plastics (Turner et al., 2015).

8 The overall size of carbon reduction potential depends on the carbon content of landfilled waste as well as fossil fuel electricity generation displaced as a result of energy-from-waste electricity generation. In the case of UK, for example, Jeswani et al. (2012) concluded that incineration with energy recovery offers significant GHGs savings of 129 ktCO2e per year even when compared to landfilling with biogas recovery.

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4.4 Adaptation and other co-benefits Jamaica’s policy commitments, project and other actions in the waste sector will likely delivery wider environmental and societal benefits. Sustainable and integrated waste management initiatives have the potential to contribute to a number of Sustainable Development Goals (SDGs).

First, mitigation actions in the waste sector can address the negative impacts the sector can otherwise have on water resources. In particular, reducing waste generation, increased recycling and greater use of incineration can all alleviate the impact that existing waste management practices, such as uncontrolled dumping, have on water pollution.

An integrated waste management system and land use system could provide benefits to agriculture. For example, reducing the demand for land used for landfills could allow for more productive uses of the land, such as agriculture, to increase crop production, and improve yields. Improved waste management practices, including the use of wastewater sludge, can also improve soil quality, with positive impacts on crop yields.

Mitigation actions in waste can also lead to positive outcomes on health and infrastructure. The incineration of waste and capture of landfill gases can reduce atmospheric pollution and landfill fires, thereby improving air quality and reducing respiratory diseases that are expected to worsen as a result of climate change. In addition, wastewater sludge management can reduce the occurrence of stagnant water, thereby reducing the spread of vector-borne diseases which are exacerbated by higher temperatures. The reduction of unmanaged waste (mostly through incineration) can also reduce the blockage of roads and waterways which can exacerbate the negative impacts of floods, landslides, and severe storms.

Improved waste management policies can result in the odour reduction associated with uncontrolled waste. All technologies and practices of waste minimisation, recycling, aerobic and anaerobic biological treatment as well as controlled landfilling and wastewater treatment are likely to reduce odour associated with non-methane gases.

Finally, the reduction of unmanaged waste can provide benefits to coastal areas. It can increase the regeneration capacity of natural habitats, including fish stocks and mangroves which will become increasingly important as climate change threatens these ecosystems.

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5 Agriculture sector

The Government of Jamaica recognises agriculture as a priority policy area due to its importance in terms of employment and social development opportunities. With 18% of the active population employed in agriculture and 46% of the total population living in rural areas, agriculture is an important contributor to the country’s economic and social development. The sector is very decentralised, with over 170,000 farmers. Traditional agricultural exports, especially coffee and citrus, are in decline, and the government is making efforts to promote non-traditional export commodities such as yams, papaya, Jamaican ackee, sweet potatoes, and marine products (IDB, 2017).

The sector is very vulnerable to climate change, compounding a series of other challenges that it faces. In 2012, the agriculture sector suffered damage of almost Jamaican $1.5bn from the effects of Hurricane Sandy. As well as this vulnerability to extreme weather events, the sector faces problems from water deficiency which will be further exacerbated by climate change; high energy costs; limited market access due to an absence of a modern fresh-produce quality compliance system; and inefficient production (CDB, 2017). 5.1 Emissions trend The GHGs emissions from Jamaica’s agriculture sector have stabilised in recent years, after a drop in 2005-6. The emissions trend presented below is based on FAO statistics and shows a reduction in GHGs emissions from around 1 MtCO2e in 2003 to 0.6 MtCO2e in 2007. The trend in emissions has been relatively flat since then.

Figure 18 Agricultural emissions have remained broadly flat since 2007

Source: Vivid Economics, based on FAOSTAT (2019)

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5.2 Policy framework Jamaica has enshrined a number of policy actions relevant to mitigation efforts in the agriculture sector in various strategies and policies. Table 4 summarises these policies, which are further discussed below. The agriculture sector is also a critical component of Vision 2030 Jamaica and Jamaica’s implementation of the Sustainable Development Goals (SDGs).

Table 4 Jamaica has introduced several policies and strategies in the agriculture sector

Overarching policy framework Sector’s strategies, plans and policy frameworks

● Climate Change Policy ● Agriculture Strategy Action Plan (2009) - 10-year agriculture Framework for Jamaica development strategy underway to include a vulnerability (2015) strategy. assessment and an agribusiness strategy.

● Food and Nutrition ● Jamaica’s NDC (2015) (adaptation-mitigation co-benefits) – as part Security Policy (2013) of the Accelerating the Uptake of Climate-Smart Agriculture (2019)

Source: Hayman (2019)

5.2.1 Overarching policy framework The Climate Change Policy Framework for Jamaica (2015) contains a list of actions related to the agricultural sector. The framework sees agriculture as a priority sector with the following strategic aims of relevance to climate change:

● Developing climate change resilient crop varieties and systems that are tolerant of flooding, drought, and salinity, and based on indigenous and other varieties suited to the needs of resource poor farmers, fisheries and livestock systems to ensure local and national food security.

● Facilitating the use of water efficient agricultural methods including using permaculture technologies, intercropping, and terracing, and improving irrigation technology and water harvesting techniques.

● Facilitating the improvement of flood and heat management techniques to protect poultry and cattle from changes in climate (e.g., improve animal housing).

● Improving ecosystem resilience by implementing measures related to soil conservation, fire management, flood and erosion control, mangrove restoration and rehabilitation, and reforestation and forest conservation.

● Improving food storage systems.

● Establishing an agricultural insurance system.

● Diversifying food production techniques by expanding the use of agroforestry, aquaculture, mariculture, and aquaponics as potential adaptation measures.

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The 2013 Food and Nutrition Security Policy that aims to improve food and nutrition security through actions across four interlinked pillars. These pillars are food availability, food access, food utilisation, and stability of food supply.

The objective of the Food Stability pillar is to improve the food and nutrition security resilience of the national community to natural and socio-economic shocks and climate change. In this regard, the pillar emphasises the implementation of adaptation and mitigation strategies as a means of enhancing the stability of food security, with the following policy intent:

● Promoting the creation of an Information System for Food and Nutrition Security (ISFNS) for food security development, as well as food crisis prevention and risk management and the construction of adequate risk profiles for the main crops.

● Pursuing climate resilient development which focuses on adaptation as well as mitigation strategies for the food and agriculture sector.

● Enhancing the capacity of relevant institutions to provide climate related information in collaboration with relevant regional bodies.

● Integrating climate management considerations into the National Agricultural Disaster Risk Management Programme.

● Reducing the impact of climate change on food production.

● Utilising vulnerability analysis and mapping to provide timely nutrition and socio-economic information on vulnerable population groups to decision-makers to enable the design of more effective emergency and relief responses.

● Developing comprehensive agricultural insurance and risk transfer schemes.

● Subscribing to a national and regional disaster fund.

5.2.2 Agriculture sector’s policy framework Jamaica’s Final Draft Agriculture Sector Plan, which is one of the country’s twenty-seven sector plans from Vision 2030, provides a means for delivering on the intent of the Food and Nutrition Security Policy. The plan lists policy measures that are likely to have a positive impact on soil conversation, soil fertility, carbon sequestration, and water availability. These include:

● Integrated farm management and organic agriculture.

● Management of low-intensity pasture systems.

● Preservation of landscape and historical features such as woods, marshes and mangroves, rivers, and streams.

● Conservation of natural habitats and their associated biodiversity.

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5.3 Contribution to climate change mitigation and adaptation A number of agriculture sector projects that are contributing to both GHGs emissions reductions, carbon sequestration and enhanced climate resilience. These include:

● The Integrated Management of the Yallahs and Hope River Watershed Management Areas (Yallahs- Hope) Project. These contiguous watersheds account for around 7% of the island’s farmlands9. The project is being implemented by the National Environment and Planning Agency (NEPA). It is a five-year programme, which started in October 2014. Grant funding totalling US$3,909,441 was provided by the Global Environment Facility (GEF) through the Inter-American Development Bank (IDB). The Government of Jamaica has also provided co-financing of US$8,872,357. The project aims to improve the conservation and management of biodiversity and the provision of ecosystem services within the region by implementing sustainable agriculture (including renewable power generation), forestry, land management and livelihood practices within targeted communities. There is a focus on introducing farmers to new practices to ensure increased productivity, while minimising soil erosion, to reduce sedimentation and increase water quality. Farming practices in these areas have the potential to affect 40% of the potable water supplied to the Kingston Metropolitan Area. An initial estimate suggests that the avoided deforestation, reforestation and sustainable land management outcomes of the project

could yield emission reductions in the order of 556,061 tCO2e for the 4 years of the project

implementation. Over a 10-year time frame, this would increase to 647,571 tCO2e. The cost-

effectiveness analysis identifies a carbon cost of US$6.7/tCO2e.

● The Essex Valley Agriculture Development Project, funded by the Caribbean Development Bank, with £35.5m provided by the UK Caribbean Infrastructure Partnership Fund to CDB, focuses on ‘enhanced production and productivity of farmers in Essex Valley in a socially inclusive gender equitable and climate sensitive manner’. It comprises the following components:

 Improved Irrigation Systems: this aims to provide infrastructure and systems with the capacity to supply water to the farm gate of all farms on the approximately 700 hectares of arable land in Essex Valley in a sustainable manner.

 Enhanced Agricultural Production and Marketing Facilities and Systems: this component focuses on improving farmer compliance with food safety standards and climate smart agriculture (CSA) practices.

 Energy Efficiency (EE)/Renewable Energy (RE): This intends to develop the necessary infrastructure and systems for EE/RE solutions. The proposed renewable power system – a 3.1 MW solar system with an 850-kWh battery storage – will offset around 47% of the grid-electricity consumption of the

irrigation system, leading to annual electricity cost reductions of JMD67 million and avoided CO2 emissions of 2,374 tons per year.

9 https://www.nepa.gov.jm/new/media_centre/press_releases/releases/2019/PR20190513-yallahs- hope_project_trains_farmers_to_farm_out_the_drought.php

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 Technical Assistance: This component will include (i) a climate vulnerability assessment study to enhance the sustainability of the systems developed under the project; (ii) capacity-building support to develop gender-responsive guidelines for mainstreaming economic inclusion of vulnerable groups in agriculture; (iii) a tariff study for the National Irrigation Commission; and (iv) an operational plan to enhance the viability and sustainability of the facilities and services to be provided in the Essex Valley Area.

● Promoting community-based climate resilience in the fisheries sector project: this is a five-year project, US$4.875 million project which started in March 2018. The project seeks to build climate resilience amongst targeted fishing and fish farming communities. The key outcomes will be:

 Reduced vulnerability of the targeted fishing and fish farming communities to climate shocks.

 Diversified and strengthened livelihoods of targeted communities.

 Strengthened and climate-smart fisheries and aquaculture policy and regulatory framework.

● ‘Enhancing the Resilience of the Agricultural Sector and Coastal Areas to Protect Livelihoods and Improve Food Security’ programme: has been supported through a US$ 9.965 million grant from the Adaptation Fund. It was approved in 2012 for a duration of 3.5 years. The programme sought to protect the livelihoods and food security of people living in seven of the country’s 14 parishes by improving water harvesting and management, as well as erosion and flood control. The programme also supported climate resilient coastal management in Negril.

In addition to the above, extension officers are already incorporating practices which support adaptation and mitigation. These practices include precision agriculture; protected agriculture; supporting development of drought tolerant crops; drip irrigation; and regeneration of bauxite mines using solar pumps.

5.4 Adaptation and other co-benefits Mitigation actions have adaptation and other co-benefits in relation to water availability. More energy efficient permaculture technologies, intercropping, terracing, improved irrigation technology and water harvesting techniques can all support the more efficient use of water resources. This contributes to offsetting the impacts of climate change on water resources, thereby improving water availability and quality.

The CSA initiatives target both reducing emissions and supporting adaptation. Agroforestry practices that increase the country’s carbon sink also promote the sustainable use of land through soil conservation and improvement in soil fertility, making it more resilient to drought conditions and heavy precipitation events. In addition, aquaculture and aquaponics can diversify food production practices and are known to be less carbon intensive than traditional farming methods, with positive effects on overall soil health and reduced vulnerability. Further, reducing carbon intensive fertilisers through CSA, and their replacement by crop rotation and reduced tillage techniques, as well as an increase in the use of drip irrigation, can also improve soil health, again making the sector more resilient to droughts.

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There are also likely to be improved health outcomes. Improved food production techniques will increase productivity and crop yields, reducing the severity of food shortages and improving food security, with positive impacts on nutrition. More sustainable agriculture land use, especially through a reduction in the use and wastage of water, can also reduce the risk of spreading of vector-borne diseases.

Finally, Jamaica’s mitigation policies in agriculture should benefit infrastructure and coastal ecosystems. Better land management will reduce sedimentation and runoff (e.g. diversion ditches) which can have a positive impact on infrastructure downstream. In coastal areas, agroforestry techniques can interact with mangrove systems. Salt-tolerant crops can be grown alongside mangroves, which increase the country’s carbon sink while also providing protection against the impacts of sea level rise.

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7 Annex: Energy model technical description

This annex section provides further detail on the modelling approach taken with respect to energy sector as well as underpinning data sources and assumptions for baseline and NDC scenarios (sections 7.1-7.3). Further, it provides further forestry analysis and compares the forestry baseline with the TNC trajectory (section 7.4). 7.1 Model description The climate mitigation impact of policies in the energy sector is estimated using the Long-range Energy Alternatives Planning system (LEAP) model. The LEAP model is an integrated energy-environment tool used to conduct energy policy analysis and climate change mitigation assessment developed by the Stockholm Environment Institute (SEI). This framework is used to model policy scenarios (based on the agreed policy packages) against a baseline to determine the emissions and other energy sector impacts (for example, the change in energy mix) of the policies. The model is highly flexible and therefore is suitable for developing countries where the availability of data can be limited. The model scope covers energy demand, energy supply, resources, environmental loadings, cost-benefit analysis and non-energy sector emissions. Figure 21 shows the flow of calculations through the LEAP model.

Figure 19 LEAP model Structure

Source: SEI

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The LEAP model can capture the effects of several policy changes at once within a particular scenario, and therefore the emissions impact of commitment packages. The team developed commitment packages based on the conditional and unconditional policy commitments identified earlier in the analysis. Using a ‘package’ approach to model the emissions impact of conditional and unconditional commitments ensure we account for the interaction between policies and therefore provide a more accurate view of the abatement potential in the energy sector. For example, when modelling multiple policies which improve the efficiency of electricity use, we will ensure that the reduction in emissions take account of other policies that are also changing the carbon intensity of the grid over time.

The LEAP model is an energy systems model and only accounts for system costs and not economic impacts. LEAP model is an energy-systems model and only considers the direct impact of the policies known as the system costs. The wider economic impacts of these changes in the energy sector, for example the macroeconomic impacts of higher or lower energy prices or lower air pollution impacts are not captured in the model.

The LEAP model is built with a branch structure as shown in Figure 20 where associated variables are stored within each branch. In section 7 the assumptions used for each branch in the baseline are detailed.

Figure 20. Variable branches of the LEAP model

Source: SEI/Vivid Economics 7.2 Modelling approach The energy commitments were represented as inputs in LEAP model by setting levels of uptake, energy efficiency improvements and target years. This was then combined with underpinning baseline trends to estimate the policy impact on energy use and GHGs emissions.

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Figure 21 Schematic description of energy policy modelling

Source: Vivid Economics

7.3 Data sources and assumptions used for energy baseline scenario As part of our assessment, we reviewed, updated and expanded on the data used in the energy sector modelling. To develop most accurate energy and emissions baseline and policy scenarios, we reviewed existing data sources and expanded on these with latest publications and relevant research sources. This included a review and update of socioeconomic, production, energy technology and costs statistics. We have also assessed the energy modelling assumptions against the latest evidence and amended these where appropriate.

The following tables provide details on the main data inputs that were updated, including the rationale and data source. We split the updated data inputs to several main topics: socioeconomic; economic; industry; transport; commerce and services; residential; energy fuel consumption; electricity consumption; electricity production, transmission and distribution; raw resources.

Table 5 Main socioeconomic variables updated in energy model

Variable Value Note Data source

Statistical Institute of Historical population 2,726,667 (2018) Jamaica

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Variable Value Note Data source

applied growth rate United Nations, World Population outlook 2,831,759 (2030) from UN data on Population Prospects historical data. 2019.

World Bank, World Urban and rural 56% and 44%, projected to follow Urbanization Prospects: demographics respectively (2018) existing trend 2018.

data source used for JPS electric utility 657,977 (2018) total, residential and JPS operational data customers (total) commercial sectors.

Source: Vivid Economics

Table 6 Key economic variables updated in the energy model

Variable Value Note Data source

Total gross domestic 2,053,191 (2019, at projected to follow Statistical Institute of product and value market prices, US$ existing trend Jamaica added by industries million dollars)

real 2010 US dollars World Bank Commodity Sugar price, world 0.27 (real 2010 US$/ were converted to 2015 Price Data; November market metric tonne, 2018) prices and projected to 2019 follow existing trend

real 2010 US dollars World Bank Commodity Aluminium price, world 2,070 (real 2010 US$/ were converted to 2015 Price Data; November market metric tonne, 2018) prices and projected to 2019 follow existing trend

nominal prices were US Geological Survey, Cement price, world 126.5 (nominal US$/t, converted to 2015 real 2019 Mineral market 2018) prices and projected Commodity Summary following existing trend

Source: Vivid Economics

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Table 7 Main industry variables updated in the energy model

Variable Value Note Data source

Alumina produced and 1,782,373 and projected to follow The Jamaica Bauxite bauxite equivalent of 4,869,056 (2017, Mt) existing trend Institute alumina produced

2018 value estimated Statistical Institute of 81,165 (2018 estimate, based existing data. Jamaica (2000-17); Sugar production tonnes) Projected to follow Sugar Industry Authority existing trend (2006-18)

Source: Vivid Economics

Table 8 Main transport variables updated in the energy model

Variable Value Note Data source

Motor cars: 472

2019 estimate per Total Number of Motor trucks: 63 vehicle type is based on Registered Motor the 2019 number of Vehicles by Vehicle & Historical consumption registered vehicles, Fuel Type – Tax of motor cars, motor Motor tractors: 0.1 estimated fuel economy Administration Jamaica; trucks, tractors and and 2018 transport Energy Balance 2018 motorcycles – diesel energy consumption data – Jamaican Ministry Motorcycles: 9 data; projected to follow of Science, Energy & existing trend Technology (ktoe, 2019 estimate)

Motor cars: 430 Historical consumption of motor cars, motor Motor trucks: 243 as above as above trucks, tractors and Motor tractors: 5 motorcycles – gas (ktoe, 2019 estimate)

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Variable Value Note Data source

Source: Vivid Economics

We estimate a reduced use of street lighting in the commerce and services sector of 13 hours per day for each type of lamp bulbs. In 2015, comparing calculated electricity use (56,466 MWh) of 12 hours street lighting and recorded electricity consumption (70,920 MWh) of street lighting implied 15 hours use per day. The recorded consumption has since then fallen to 62,423 MWh. Assuming the same number and distribution of lamp bulb types as in 2015, this means that we estimate around 13 hours use a day for each type of light bulb (Table 9).

Table 9 Main updates to commerce and services data

Variable Value Note Data source

Street lighting – high JPS Electricity Statistics, 13.3 hours of daily use pressure sodium (HPS) see text above the table 2018 estimate of street (2018 estimate) lamps lighting electricity sales

JPS Electricity Statistics, Street lighting – mercury 13.3 hours of daily use see text above the table 2018 estimate of street vapour lamps (MVL) (2018 estimate) lighting electricity sales

Street lighting – light- JPS Electricity Statistics, 13.3 hours of daily use emitting diodes (LED) see text above the table 2018 estimate of street (2018 estimate) lamps lighting electricity sales.

this is further split to share updated based on HPS lamps – share of 42% (2019) 70W, 100W, 150W, email communication total lamps 250W and 450W types with MSET

this is further split to share updated based on MVL lamps – share of 3% (2019) 125W, 160W, 250W and email communication total lamps 400W with MSET

share updated based on LED lamps – share of this is further split to 55% (2019) email communication total lamps eight LED type lamps. with MSET

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Variable Value Note Data source

71,926 (2018, million projected to follow Statistical Institute of Water consumption gallons) existing trend Jamaica

Source: Vivid Economics

Table 10 Main updates to residential sector data

Variable Value Note Data source

cost of State 40G water Solar water heater – 513 US$ (2020 value) heater, excluding ATL Jamaica website capital cost installation costs

US Energy Information Administration, Updated LED lamp – capital cost 5.9 US$ (2020 value) Buildings Sector Appliance and and average lamp life 48,000 hours Equipment Costs and Efficiencies

US Energy Information Administration, Updated Buildings Sector CFL lamp – capital cost 1.9 US$ (2020 value) Appliance and

Equipment Costs and Efficiencies

Source: Vivid Economics

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Table 11 Main updates to energy fuel consumption data

Variable Total sectoral demand (ktoe, 2018) Note Data source

Cement 69 industry10

Alumina 675 processing

Bauxite and 54 mining Energy Balance Construction 2018 data – 28 industry projected to follow Jamaican existing trend Ministry of Science, Energy Sugar industry 111 & Technology Other 74 manufacturing

Commerce and 291 services

Agriculture 24

Transport 686

Source: Vivid Economics

Table 12 Main updates to electricity consumption data

Variable Value (2018, GWh) Note Data source

2018 value based on 2008-2013 from Energy Construction sector 30 projecting an existing Balance data trend

10 For each sector, this covers petroleum products (e.g. gasoline, diesel oil, kerosene, fuel oil, LPG, lubricants) as well as renewable sources (fuelwood, charcoal, hydro, wind, solar PV and bagasse) where applicable.

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Variable Value (2018, GWh) Note Data source

2018 value based on 2008-2013 from Energy Sugar sector 36 projecting an existing Balance data trend

2006-2016 from Energy Balance data; 2015 and projected to follow 2016 estimates based Water sector 211 existing trend on National Water Commission’s Annual Report 2016-17

projected to follow 2008-2018 from Energy Alumina processing 877 existing trend Balance data

Other manufacturing projected to follow 2008-2018 from Energy 363 sector existing trend Balance data

Source: Vivid Economics

Table 13 Main updates to electricity production, transformation and distribution data

Variable Value Note Data source

calculated as the proportion of transmission and Electricity transmission 23.3% (2008), 29.5% Energy Balance Data distribution losses and and distribution losses (2018) 2008-2018 electricity generation; projected to follow existing trend

Electricity generation - 13.4 (ktoe, 2008) projected to follow Energy Balance Data hydroelectric power 15.1 (ktoe, 2018) existing trend 2008-2018

Electricity generation – 4.2 (ktoe, 2008) projected to follow Energy Balance Data wind power 25.5 (ktoe, 2018) existing trend 2008-2018

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Source: Vivid Economics

Table 14 Main updates to raw resources price data

Variable Value Note Data source

0.41 (2019); 0.53 (2028) ethanol wholesale price, OECD-FAO Agricultural Ethanol price (nominal US$/gallon) United States, Omaha Outlook 2019-2028

biodiesel: Producer 0.87 (2019); 0.95 (2028) price, Germany, net of OECD-FAO Agricultural Biodiesel price (nominal US$/litre) biodiesel tariff and Outlook 2019-2028 energy tax

World Bank Commodity 61.4 (2019); 70 (2030) Markets: Historical Crude oil price Annual Prices; World (nominal US$/bbl.) Bank Commodity Markets: Price Forecasts

World Bank Commodity 10.6 (2019); 8.5 (2030) Markets: Historical LNG price natural gas LNG, Japan Annual Prices; World (nominal US$/MMBtu) Bank Commodity Markets: Price Forecasts

76.8 (2017 real JD/litre) projected to follow Petrojam: Historical Gasoline 90 price existing trend Prices, Price Index

projected to follow Petrojam: Historical Kerosene price 62.9 (2017 real JD/litre) existing trend Prices, Price Index

average of propane and 0.26 (2015 real Petrojam: Historical LPG price butane prices. Projected US$/litre) Prices, Price Index to follow existing trend

projected to follow Petrojam: Historical Auto diesel price 128 (2017 real JD/litre) existing trend Prices, Price Index

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Variable Value Note Data source

HFO fuel price. Petrojam: Historical Residual fuel price 66.4 (2017 real JD/litre) Projected to follow Prices, Price Index existing trend

77.9 (2019); 60 (2030) World Bank Commodity Markets: Historical based on Australian coal Bituminous coal price Annual Prices; World price at world markets (nominal US$/metric Bank Commodity tonne) Markets: Price Forecasts

Source: Vivid Economics

7.4 Modelling parameters underpinning energy commitments This section provides details on the parameters used across modelled energy sector commitments including their main values, sources and any additional notes.

Table 15 Modelling parameters for Net Billing Mitigation Action

Parameter Value Source Notes

Distributed solar PV National Renewable Energy 20 years lifetime Laboratory, 2019

Distributed solar PV Varies across the year MEGJC, 2018 availability factor

Distributed solar PV Based on availability 0% Assumption capacity credit at time of peak load.

From 2,770 US$/kW in Distributed solar PV National Renewable Energy 2017 to 1,070 US$/kW overnight capital cost Laboratory, 2019 in 2050

From US$23/kW-yr. in Distributed solar PV fixed National Renewable Energy 2017 to US$11/kW-yr. O&M cost Laboratory, 2019 in 2030

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Parameter Value Source Notes

Distributed solar PV National Renewable Energy 0 US$/MWh variable O&M cost Laboratory, 2019

Office of Utilities Regulation, Distributed solar PV WACC 11.95% 2010

Distributed solar PV 10 years Assumption construction loan period

Cost of administering Net 90.6 US$ / kW Doris et al, 2015 Billing program

Table 16 Modelling parameters for reduction in T&D losses

Parameter Value Source Notes

Reduction in Ministry of Energy 1.50% technical losses and Mining, 2010a

Reduction in Ministry of Energy non-technical 2.60% and Mining, 2010a losses

Implementation 70.7 million Ministry of Energy Assumed to be a one-time cost to establish new cost US$ and Mining, 2010a norms and systems between 2017 and 2020.

Total Reduction Ministry of Energy Assumed the project starts in 2017. Total 4.10% in losses and Mining, 2010a reduction achieved in 2020.

Table 17 Modelling parameters for switch to LNG in Alpart refinery

Parameter Value Source Notes

Residual fuel

0.489 US$/litre PetroJam, 2019 oil price

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Parameter Value Source Notes

10.56 World Bank Commodity Prices, LNG price US$/million 2019a BTU

Fuel switching 2020 Defined in policy year

Alpart share of national Assumes production in line 44% Calculation based on capacities alumina with capacity. production

Clarendon 1.416 million Jamaica Bauxite Institute capacity tonnes

Ewarton 0.6 million Alumina Jamaica Bauxite Institute tonnes capacity

Nain St. Elizabeth 1.65 million Jamaica Bauxite Institute (Alpart) tonnes capacity

Table 18 Modelling parameters for shift to T8 lighting

Parameter Value Source Notes

Electricity consumption for 5,026 MEGJC, 2018 2010 value for education institutions. lighting, education MWh

Electricity consumption for 6,730 MEGJC, 2018 2010 value for healthcare institutions. lighting, health MWh

T8 vs. T12 saved electricity 903 MEGJC, 2018 2010 value for education institutions. consumption, education MWh

T8 vs. T12 saved electricity 1,412 MEGJC, 2018 2010 value for healthcare institutions. consumption, health MWh

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Parameter Value Source Notes

Capital + installation cost of switching Total investment cost, 106,007 MEGJC, 2018 existing (2010) T12 to T8 bulbs. Cost per education US$ saved energy also used for hospitals.

10.01 24,000-hour lifetime, 2,397 annual hours T8 bulb lifetime MEGJC, 2018 years of operation.

Table 19 Modelling parameters for shift to smart LED street lighting

Parameter Value Source Notes

Capital and 70 - 400W high pressure 177 – 210 US$ MEGJC, 2018 installation cost sodium (HPS) lamps.

125 - 400W mercury vapor Capital and 320 US$ MEGJC, 2018 lamps (MVL) and 160W installation cost tungsten lamps.

Capital and Rowe, 2017 – meeting with 290 – 385 US$ 43 - 161W LED lamps. installation cost JPS July 12 2017

New York State Energy LED lamps only, expressed O&M savings 50 US$/year Research and Development relative to other Authority, 2014 technologies.

HPS lamps, average of two Lifetime 22,000 hours MEGJC, 2018 estimates.

Lifetime 28,000 hours MEGJC, 2018 MVL and tungsten lamps.

LED lamps, estimate based Lifetime 60,000 hours MEGJC, 2018 on range of lifetimes.

Reduction in usage Relative to conventional 55% Lau et al., 2015 for smart streetlights management scheme.

Approximately Source also provides break- Schedule for LED Rowe, 2017 – meeting with 35,000 lamps/year down of new lamps by upgrades JPS July 12 2017 through 2020 wattage.

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Parameter Value Source Notes

Correspondence with the Number of Lamps 105,000 Ministry of Science, Energy (2017) and Technology

Table 20 Modelling parameters for LNG buses

Parameter Value Source Notes

11,242 gallons Diesel consumption Alternative Fuels Data US estimate. Local data for Jamaica of gasoline per conventional bus Center, 2016 could not be located. equivalent/year

Fuel savings, from 25% MEGJC, 2018 diesel to LNG

Incremental cost of 26,700 US$ MEGJC, 2018 LNG vs. diesel bus

Investment cost of 113,636

Mitchell, 2015 Average cost of CNG-fuelling station. filling station US$/bus

LNG bus lifetime 15 years MEGJC, 2018

Filling station lifetime 20 years MEGJC, 2018

Table 21 Modelling parameters for B5 blending

Parameter Value Source Notes

Production efficiency 64.98% MEGJC, 2018 Jatropha pathway.

Assumption, based on Production capacity six-month crushing 50% Jatropha pathway. availability factor season for sugarcane: Landell Mills, 2011

WVO pathway. Auxiliary energy use 0.0752 GJ/GJ produced MEGJC, 2018 Composed of methanol (64.7%), natural gas

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Parameter Value Source Notes

(30.7%), and electricity (4.6%).

Jatropha pathway. Includes auxiliary requirements from WVO pathway, plus 0.0549 GJ Auxiliary energy use 0.1301 GJ/GJ produced MEGJC, 2018 natural gas per GJ biodiesel produced required for oil extraction from jatropha plant.

0.81 US$/litre biodiesel Total cost of production in 2019, reaching 0.75 OECD-FAO, 2019 US$/litre in 2030

First year of production 2020 Assumption

Table 22 Modelling parameters for implementation of CHP in alumina refineries

Parameter Value Source Notes

Increase in cogenerated electricity, 10% of fuel Watson, 2010 by 2030 oil inputs

Current combined average 75-85% Watson, 2010 efficiency

Targeted efficiency (sector-wide) 90% Watson, 2010

It is not clear that the costs of an efficient unit would be different Investment cost, O&M cost - Assumption than for any modern unit used to replace existing equipment at the end of its life.

Timeline for implementation 2020 - 2030 Assumption

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Parameter Value Source Notes

Ratio between old and new Reduction in energy intensity 12.5% Calculation energy efficiency

Table 23 Modelling parameters for reduction in water loss in Kingston

Parameter Value Source Notes

Share of NWC electricity used for water 90% MEGJC, 2018 distribution

Share of NWC water supplied to consumers 22% MEGJC, 2018 in Kingston/St. Andrews Parish

Implementation costs are not specific enough Total investment cost 4900 JAM million Smith-Edwards, 2015 for annualization, so they are spread equally among six project years.

Total investment cost 37 US$ million Smith-Edwards, 2015 (US$)

Share of loss (2015) 53% MEGJC, 2018

Share of loss (2018) 49% Jamaica Observer, 2018

Share of loss (2020) 37% MEGJC, 2018

Share of loss (2021) 30% MEGJC, 2018

Share of loss (2023) 20% MEGJC, 2018

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Table 24 Modelling parameters for energy efficiency within EMEP

Parameter Value Source Notes

Electricity consumption of the 73 facilities to be 31,377,402 kWh/yr. JICA, 2017 retrofitted (2017)

Electricity consumption of the 73 facilities to be 16,004,807 kWh/yr. JICA, 2017 retrofitted (2023)

Timeline 2023 JICA, 2017

Budget is for the full EMEP, and includes other Inter-American commitments e.g. Urban Budget 24.6 US$ million Development Bank, Traffic Management (UTMS) 2016 and technical capacities of MSET

Table 25 Modelling parameters for the KMA Urban Traffic Management System

Parameter Value Source Notes

Ministry of Science, Kingston traffic fuel 40% Energy and consumption decrease by Technology

Jamaica cars per people 0.20 cars / capita Calculation

Kingston and St Andrew Statistical Institute 669,773 Population Jamaica

Estimated number of cars in Kingston and St 135,911 Estimate Andrew

Population in Jamaica in Statistical Institute 2,726,667 2018 Jamaica

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Parameter Value Source Notes

Number of cars in Tax Administration 553,300 Jamaica 2018 Jamaica

Share of all cars in Statistical Institute 24.6% Kingston & St Andrew Jamaica

Target year for policy 2023 JICA Jamaica, 2017

Budget is for the full Inter-American EMEP, and includes other Budget 3.7 US$ million Development Bank, commitments e.g. energy 2016 efficiency and technical capacities of MSET

Table 26 Modelling parameters for the EECP

Parameter Value Source

Budget 20 US$ m MSET, 2019

Savings 2.2 US$ m MSET, 2019

3,500 barrels Reduced oil consumption Williams, 2019 equivalent

Table 27 Modelling parameters for modelling the water NAMA

Parameter Value Source

Reduction in electricity intensity – 10% UNDP (2019) Intervention 1

Electricity savings from energy efficiency 19,800 MWh UNDP (2019) projects – Intervention 2

Cost – Intervention 1 25.3 million USD UNDP (2019)

Cost – Intervention 2 35.4 million USD UNDP (2019)

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8 Annex: Energy sector commitments

In this section of the Annex we describe in detail the energy commitments which have been modelled.

8.1.1 Net billing This action assumes that 12.8 MW of distributed solar PV capacity is deployed under MSET’s Net Billing program by 2020, an increase of 12.1 MW relative to 2013. The Net Billing program was piloted from 2012-2015 (Doris et al. 2015), then was updated and resumed in 2016 (MEGJC, 2018). In the pilot phase 1.4 MW of distributed solar PV capacity was connected to the grid. According to MSET’s Registry of Net Billing Licenses, 0.72 MW of this capacity connected in 2012 and 2013 (MEGJC, 2018). The overall cap for grid-tied capacity under the Net Billing program is 12.8 MW.

The electricity production impacts of net billing (and other distributed generation) capacity deployed through 2013 are captured in the baseline scenario. The net billing action in the NDC scenario relative to the baseline is therefore deployment of 12.08 MW of capacity during 2014-2020 (and rebuilt as needed thereafter to be operational through 2055). This capacity is assumed to be distributed solar PV whose output is not subject to normal transmission and distribution losses because the generation is at the customer premises.

Changes to costs are captured through capital, installation, O&M and administration costs of distributed solar, weighed against avoided investment and generation costs for the grid. As Doris et al. (2015) suggest, deployment through net billing is not expected to necessitate additional grid investments relative to the baseline. Electricity generation by net billing installations reduces the need for centrally produced electricity, changing energy use, emissions, and costs in the electricity sector. New costs are incurred for the distributed solar PV equipment, its installation and O&M, and administration of the Net Billing program.

8.1.2 Reduced T&D losses In this planned action, losses in electricity T&D are reduced by 4.1% of net generation by 2020. This reduction in losses is held fixed from 2020, relative to the baseline. Technical losses decrease by 1.5%, and non-technical losses are lowered by 2.6% (Ministry of Energy and Mining, 2010a). Upgraded billing, inspection, and enforcement systems produce these outcomes, which lead to emissions and cost savings in electricity production.

8.1.3 Alpart refinery - LNG For this planned action, the Alpart refinery switches from using fuel oil to LNG in 2020 following closure for modernization in 2019. Capital and O&M costs for equipment are assumed not to change, consistent with the overview of CHP technology costs in U.S. Environmental Protection Agency (EPA, 2017b). The overall energy intensity of production with LNG is assumed to be equivalent to the energy intensity of production with fuel oil, and the share of production conducted in each refinery is assumed to be in line with capacity.

Energy efficiency improvements in the alumina sector are considered separately in the CHP mitigation action.

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8.1.4 T12 to T8 lighting This planned action assumes that schools and hospitals convert from 40W T12 lighting to 32W T8 fluorescent lighting by 2030. Electricity demand is reduced, lowering energy use, emissions, and costs in electricity production. The measure is adapted directly from a 2010 audit of public-sector electricity consumption, scaled as necessary to projected electricity demand in the future. The audit quantified the achievable energy savings from switching to T8 linear fluorescent lighting in the two public subsectors. Direct investment costs are annualized over the lifetime of the new lighting technology.

8.1.5 Street lighting Based on Jamaica Public Service’s (JPS) Smart LED program, this planned action involves upgrading all 105,000 grid-connected streetlights to LEDs between 2018 and 2020, at a rate of approximately 35,000 per year. Compared to conventional lamps, the LED lamps use less electricity, reducing energy inputs, emissions, and costs in electricity production. In addition, a sensor network is deployed alongside the new lamps, dimming (or switching off) the lamps as needed depending on pedestrian and motorist traffic to reduce the number of hours of daily usage.

8.1.6 LNG buses This action introduces 136 LNG buses in the Kingston and Montego Bay area to replace conventional diesel buses. The Jamaica Urban Transit Company (JUTC) is embarking on plans to introduce LNG buses into its fleet. The number includes 36 buses in Montego Bay (Jamaica Observer 2017) and 100 buses in the Kingston region. After 2025, the share of LNG among all fuels consumed for road transport is held constant, meaning that the number of LNG buses grows at the same rate as final energy demand for road transport.

The introduction of LNG buses could reduce both energy consumption and emissions from the transport sector. Although natural gas-powered vehicles tend to be less fuel efficient than their conventional counterparts (MEGJC, 2018), the age of JUTC’s diesel fleet means that switching from diesel to natural gas buses can reduce energy consumption by up to 25%.

8.1.7 B5 blending This action introduces domestic biodiesel production in 2020, blended 5%-by-volume with ordinary diesel fuel for all end uses in the transport, industrial, commercial, residential, and agricultural sectors. Biodiesel production using non-food crops grown on marginal lands is of national interest. Castor and jatropha are the main crops proposed for production (Ministry of Energy and Mining, 2010c), with processing of Waste Vegetable Oil (WVO) into biodiesel also being explored on a demonstration basis. While no commercial-scale biodiesel producers are currently operating in Jamaica, a B5 fuel mandate is under development (Bandy, 2016) and should coincide with commissioning of the country’s first major production facility.

Under this planned mitigation action, biodiesel is produced from the oily seeds of the jatropha plant. Jatropha- to-biodiesel production capacity is deployed as needed to support the B5 standard.

8.1.8 Combined heat & power (CHP) in alumina refineries Under this action, energy efficiency in alumina refineries increases from 75-85% in 2020 to 90% by 2030 following the introduction of steam turbine CHP technologies. According to Watson (2010), the current

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technologies deployed in the alumina sector provide a combined average efficiency of between 75% and 85%, and the Government of Jamaica plans to improve this to 90% sector-wide. We model the change as an increase in useful energy outputs of 10%. The increase is assumed to arise due to replacement of existing, older equipment with more efficient CHP technologies. The newer, more efficient equipment is not expected to cause early retirement of existing assets. Instead, the measure is assumed to be introduced gradually as the old equipment naturally retires through 2030. The additional cogenerated electricity displaces grid-produced electricity with attendant cost, energy, and emissions impacts.

8.1.9 Kingston reduced water distribution loss In this action, water losses are translated into reduced electricity consumption for the National Water Commission (NWC). The NWC is the single largest user of electricity in Jamaica (Ministry of Science, Energy and Technology), with most of its consumption used for water pumps, motors, and drives. In this planned action, reductions in water distribution losses in Kingston and St. Andrews Parish are assumed to have a proportional impact on NWC’s electricity consumption. The measure is introduced in 2018, reducing lost water from 53% of production in the baseline to 37% in 2020, 30% in 2021, and 20% in 2023.

8.1.10 Integrated Resource Plan (IRP) The IRP’s deployment targets for new capacity between 2018 and 2030 are used as inputs for this commitment. Jamaica’s IRP is a comprehensive decision support tool and road map for meeting Jamaica's electricity grid objectives over the next 20 years. The IRP produced several relevant outputs which have been utilised in the NDC modelling process, such as generation infrastructure parameters on expected lifetimes and costs. However, the key integration of the commitment in the NDC modelling process is the deployment schedule for Jamaica between 2018 and 2030, including the size of new generation facilities (MW) and their year of activation.

8.1.11 Energy Management & Efficiency Programme (EMEP) – Energy efficiency This action reduces electricity consumption within public sector health, education, and public administrative facilities. As part of the commitment, 73 large facilities are to be retrofitted by 2023, reducing electricity consumption in these facilities by over 15 million kWh annually (JICA, 2017). Projections for electricity consumption are based on bottom up analysis conducted by the Japan International Co-operation Association (JICA).

This action is internationally funded and is therefore included in the conditional NDC. JICA, the Inter-American Development Bank (IDB) and the European Union Caribbean Investment Facility (EU-CIF) funded the EMEP (PCJ, 2018).

8.1.12 EMEP – Urban Traffic Management System (UTMS) The UTMS aims to reduce fuel consumption in the Kingston Metropolitan Area (KMA) transport sector by 40% relative to the baseline. This will be achieved through the installation of devices such as optical fiber cables and traffic lights and by delivering the relevant training for implementing the UTMS.

This is modelled by reducing end fuel consumption in line with the share of all vehicles in the KMA. To estimate the impact of a 40% reduction of traffic fuel consumption in the KMA, we first break out the share of population

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in the area and assume a level of car ownership in line with the national average. These vehicles then reduce their fuel consumption by 40%, leading to a reduction in total energy consumption in the road transport sector.

This action is internationally funded and is therefore included in the conditional NDC. JICA, the IDB and the EU- CIF funded the EMEP (PCJ, 2018).

8.1.13 Energy Efficiency and Conservation Project (EECP) This action involved the deployment of energy efficiency measures in public facilities. The EECP was a project funded by the IDB and launched in 2012, with a budget of around US$20 million. Specific measures include replacing inefficient lighting with more efficient technologies, such as LED, replacing inefficient mini-split Air Conditioning (AC) with inverter-based mini split units and/or AC central units, window tinting, window sealing, roof insulation and automatic door closers. The scheme also encourages demand side management technologies such as smart grid or metering (GoJ, 2011). Between 2013 and 2018, the EECP was involved in the installation of:

● over 80,000 sq. ft. of heat reducing film at 37 public sector facilities;

● over 200,000 sq. ft. of cool roof solutions at 11 public facilities; and

● energy efficient air-conditioning solutions at 25 facilities.

From 2013-2018, the EECP interventions saved the Jamaican Government US$2.2 million, reducing oil consumption by 3,500 barrels of oil equivalent (MSET, 2019). These figures are used in conjunction with the budget of US$20 million to generate overall cost and emissions impacts through the model.

This action is internationally funded and is therefore included in the conditional NDC. The IDB funded the EECP (PCJ, 2018).

8.1.14 Water NAMA The water NAMA lays out two interventions to reduce electricity consumption in the water sector:

● Intervention 1 involves the development of 60 solar PV plants, increasing the share of captive-use renewable generation to 10% in the water sector. This gradually reduces electricity intensity in the sector between 2020 – 2030, reaching a 10% reduction by 2030. This assumes that the 60 solar PV plants are solely used to reduce the water sectors electricity consumption and excess electricity is not sold to the grid.

● Intervention 2 includes 55 energy efficiency projects expected to lead to electricity savings of 19,800 MWh per annum.

We have modelled the commitments as the following:

● We model the first intervention as a 10% reduction in electricity intensity by 2030 in the water sector.

● In the second intervention there is a reduction in electricity intensity such that there are annual savings of 19,800 MWh by 2030 in the water sector.

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● Implementation of these interventions is continuous between 2020 and 2030 as per the commitment description, and so electricity consumption in the water sector reduces gradually during the period relative to business as usual.

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9 Annex: LUCF sector - additional analysis and comparisons with the TNC

This section compares the baseline results presented above with results from the Third National Communication of Jamaica (TNC). The TNC figures were calculated based on the 1998 LUCA, and as a result are not aligned with the figures calculated in the above analysis. This section compares our results for 2012 emissions with the TNC figures and explains the driving factors behind these differences.

In the Third National Communication, land-use change and forestry emissions are held constant between 2012 and 2030. Whilst the TNC gives emissions figures to 2030, the emissions between 2012 and 2030 are held constant. This simplified assumption reflected that the forestry sector was not included in the original NDC. By contrast, as explained above, the figures presented as part of this analysis reflect a bottom up analysis of land use change and forestry emissions.

Figure 22 Land use change and associated emissions are modelled annually to 2030 in our analysis

Source: Vivid Economics

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Breaking down net emissions shows more differences between our analysis and the TNC analysis. In both 2012 and 2030, emissions due to forest removal are significantly higher in our analysis. This is due to higher levels of annual forest removals in the 2013 LUCA than used in the TNC calculations. In addition, sequestration from existing forest land is higher than in the TNC, due to the identification of additional forest cover in the 2013 LUCA.

Figure 23 Emissions due to forest removal are significantly greater under our analysis

Source: Vivid Economics

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Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis

Assessment of Jamaica’s Climate Change Mitigation Potential and Implications for its Updated NDC - Sectoral Modelling and Analysis