Adv. Geosci., 45, 267–272, 2018 https://doi.org/10.5194/adgeo-45-267-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Evolution of seaports of the Russian Far East in relation to changes in the energy structure in Pacific Asia Sergey Venevsky1 and Elena Zaostrovskikh2 1Department of Earth System Sciences, Tsinghua University, Beijing, China 2Economic Research Institute FEB RAS, Khabarovsk, Russia Correspondence: Sergey Venevsky ([email protected]) Received: 7 May 2018 – Revised: 2 August 2018 – Accepted: 6 August 2018 – Published: 10 September 2018 Abstract. We estimated the current state of seaports Vanino Coal is transported to China from Russia through seaports and Sovetskaya Gavan, situated in the Russian Far East, as of the Russian Far East, particularly in Khabarovsk Krai beneficiaries to themselves (at local scale), to Khabarovsk (see Fig. 1a). The seaports of Vanino and Sovetskaya Gavan Krai (regional or provincial scale) and to the Russian Feder- are all-year-round ports in Khabarovsk Krai (see Fig. 1b), ation (inter-regional or federal scale). Further, we make pro- specializing in different cargo types, but mainly coal, with jections for the near future (until the year 2030) for condi- catchment areas for coal mainly situated in eastern Siberia tions of current export fuel supply demands in China and for and the far eastern federal districts (see Fig. 1c). Further conditions of climate-friendly energy restructuring in China. prospects for the development of the seaports of Vanino and It is shown that the coal specialization of Vanino and Sovet- Sovetskaya Gavan will primarily be connected with the Rus- skaya Gavan seaports will not be profitable in the near future sian national project “Free Port of Vanino”, which will later (year 2030) for conditions of climate-friendly energy restruc- spread to the port of Sovetskaya Gavan. The main idea of the turing in China. There will be considerable economic losses project is the development of industrial exports based on the at the national level if coal specialization is persistent. Thus, potential of the Khabarovsk territory. It is expected that its environmental policies regarding energy structure in a coun- implementation will modify the economy of the Khabarovsk try with a large economy may sufficiently influence the de- Krai region. However, changes in export supply needs may velopment of transport, industry and urban infrastructure on override benefits to the province. an inter-regional, regional or local level for a country import- In this study, we focused on the seaports of Vanino and ing fuel resources. Sovetskaya Gavan as beneficiaries at different spatial scales (a) at the local scale (i.e., to themselves), (b) at regional or provincial scale (i.e., to Khabarovsk Krai) and (c) at inter- regional or federal scale (to the Russian Federation). Further, 1 Introduction we make projections until the year 2030 for (a) current export fuel supply needs in China and (b) climate-friendly energy It has been long anticipated by researchers and policy mak- restructuring in China. ers that climate-friendly restructuring in the energy sector of one country may have long-lasting consequences for the eco- nomic, social and environmental situation in another coun- 2 Methods try importing fuel. The socioeconomic effects of decreasing fuel imports can have different consequences in the import- We took tax to profit, number of persons employed and in- ing country at inter-regional (national), regional (provincial) vestments for development of port infrastructure as optimiza- and local scales. Represented here is a case of China’s en- tion variables of the study. Initial data sources were Rus- ergy sector restructuring and Russian import of coal. Russia sian state Statistics and Chinese state Statistics for the years has now changed priorities due to the geopolitical situation 2005–2013. and is now focused to the east (China, Japan, South Korea). Published by Copernicus Publications on behalf of the European Geosciences Union. 268 S. Venevsky and E. Zaostrovskikh: Evolution of seaports of the Russian Far East Figure 1. Adv. Geosci., 45, 267–272, 2018 www.adv-geosci.net/45/267/2018/ S. Venevsky and E. Zaostrovskikh: Evolution of seaports of the Russian Far East 269 Figure 1. Geographical setting of the study: (a) seaports of the Russian Far East (distances are given in kilometers); (b) seaports of Vanino and Sovetskaya Gavan; (c) coal mining areas supplying the seaports of Vanino and Sovetskaya Gavan. www.adv-geosci.net/45/267/2018/ Adv. Geosci., 45, 267–272, 2018 270 S. Venevsky and E. Zaostrovskikh: Evolution of seaports of the Russian Far East Export supply needs for coal from Vanino and Sovetskaya (billions of tonnes) for China D.t/ D D.2005/ · f .t/, where Gavan were calculated from the historical need for fossil fu- function f .t/ is normalized to its minimum ratio of coal in els in China (see Fig. 1 from Lu et al., 2018) for the years total country’s energy consumption. The function can have 2005–2030, which projected into the future according to cur- a linear growth in the interval 2005–2030 when the existing rent export needs from the seaports for the years 2005–2013. trend of coal demand is assumed or it can have a maximum Total “tax to profit” amounts for the seaports Vanino and around 2020 and decreasing trend afterwards if the climate- Sovetskaya Gavan for the present were calculated for the en- friendly restructuring of energy consumption in China is as- tire cargo port throughput for categories of commodities di- sumed. vided into four classes: raw materials (including coal), mate- Cargo throughput in all categories is supposed to be lim- rials, semi-finished products and equipment (Zaostrovskikh, ited by carrying capacities of transport lines (railway and 2017a). Costs (further recalculated to tax to profit) were es- automobile for inter-regional and the Khabarovsk Krai cat- timated using matrixes of input–output commodity fluxes egories) and the port infrastructure facilities: (separately for inter-regional, regional and port level; Za- ≤ · ostrovskikh, 2017a). In order to evaluate the cost parameters, CARGOl.i/ 12 CPMl.i/; (2) all cargo was listed at 2013 prices. Population changes for the where CPMl.i/ are monthly carrying capacities for infras- present in urban settlements around the seaports were taken tructure of different categories and different commodity from the Russian Population Census (Zaostrovskikh, 2017a). types (million tonnes per month). Carrying capacities for the Total tax to profit for the future for the seaports and fu- future period are dependent on annual investment functions ture investments for ports’ infrastructure were estimated in F .I .t// according to scenarios (see description above), so two scenarios: scenario 1, in which demand in China con- that tinues slightly to decrease as now and scenario 2, in which environmental energy restructuring in China is assumed with CPMl.i/.t/ D CPMl.i/.2015/ · F .I .t//; (3) considerable decrease in the need for coal. To estimate fu- 2030 ture total cargo in Vanino and Sovetskaya Gavan, all running where F .I .2015// D 1 and I.total/ D P I .t/ is the total investment projects were assumed to be fulfilled in the near 2015 investment (in millions of rubles) according to the adopted future, 2020 (variant I), or planned new investment projects scenario. for the seaports were assumed to be accomplished up to the Cargo throughput is recalculated into the cost for process- year 2030 at its minimum (variant II) and maximum (vari- ing of commodities (millions of rubles) for category P, and ant III) extent. Variant II is subdivided further into three sub- tax to profit for categories IR and KK, using prices for the variants, according to percentage of coal in the total cargo: year 2013. IIa – the recent percentage of coal is assumed; IIb – half Number of employed people (in units of thousands of peo- of recent percentage of coal is assumed, while the amount ple) for the future period for the three different categories of other cargo types is proportionally increasing their recent (IR, KK and P) was calculated from a log-linear regression values and IIc – no coal, all other cargo types substitute it. functions N D a · exp.b · CARGO .total), a and b are re- The limitations on carrying capacity for the existing railroad l l l l gression coefficients), obtained from Russian state Statistics network were accounted for (Zaostrovskikh, 2017b). Future and the Russian Population Census for the years 2005–2015. population dynamics was estimated using nonlinear regres- The model was calibrated for the year 2005 and validated sion for persons employed by cargo throughput. for the years 2013 and 2015 using Russian state Statistics. All calculations were performed in Microsoft Excel using macros. State variables of our model are cargo throughput (mil- 3 Results lions of tonnes in four different categories for inter-regional, Khabarovsk Krai and ports of Vanino and Sovetskaya Gavan: The calculated current state of seaports Vanino and Sovet- skaya Gavan as beneficiaries to themselves, to Khabarovsk 4 X Krai and to the Russian Federation is shown in Table 1. Based CARGO .total/ D CARGO .i/; (1) i l on the estimates received, the following conclusion can be iD1 drawn: the cargo turnover of the Vanino and Sovetskaya Ga- where l are either inter-regional (IR), Khabarovsk Krai (KK) van seaports by natural indicators is developing at a more or the ports (P) throughout and i is a type of material: raw intensive rate (3 times) than the value (2.16 times) for the pe- materials (including coal), materials, semi-finished products riod 2005–2013. This occurs because the largest share is for or equipment. Current day raw materials are completely as- raw materials (coal) and in terms of value on materials (oil sociated with coal in our model, and for the future period products).