Transportation policy profiles of Chinese city clusters - guest contribution Publication Data

Authors: Responsible: This publication is a guest contribution. The origi- Alexander von Monschaw, GIZ nal article, by Nancy Stauffer, appears in the Spring 2020 issue of Energy Futures, the magazine of the Layout and Editing: MIT Energy Initiative (MITEI), and is reprinted here Elisabeth Kaufmann, GIZ China with permission from MITEI. View the original article on the MIT Energy Initiative website: http://energy. Photo credits (if not mentioned in description): mit.edu/news/transportation-policymaking-in-chine- Cover - Stephanov Aleksei / shutterstock.com se-cities/. Figure 4 - Junyao Yang / unsplash.com Figure 7 - Macau Photo Agency / unsplash.com Supplementary material is retrieved from the scien- Figure 10 - Cexin Ding / unsplash.com tific journal article „Moody Joanna, Shenhao Wang, Figure 13 - Mask chen / shutterstock.com Jungwoo Chun, Xuenan Ni, and Zhao. (2019). Figure 5, 8, 11, 14 - Maps based on freevectormaps.com Transportation policy profiles of Chinese city - clus ters: A mixed methods approach. Transportation Re- URL links: search Interdisciplinary Perspectives, 2. https://doi. Responsibility for the content of external websites org/10.1016/j.trip.2019.100053 [open access]“. linked in this publication always lies with their re- spective publishers. GIZ expressly dissociates itself Published by: from such content. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH On behalf of the German Federal Ministry of Transport and Digital Infrastructure (BMVI) Registered offices: Bonn and Eschborn GIZ is responsible for the content of this publication.

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Project: Sino-German Cooperation on Mobility and Fuels Stra- tegy (MFS) as a Contribution to the Mobility and Transport Transition In Brief Abstract 3 Transportation experts at MIT have developed Chinese cities have experienced diverse ur- building urban rail and discounting public new insights into how decision makers in banization and motorization trends that pre- transport. Sprawling, medium-wealth cities hundreds of Chinese cities design and adopt sent distinct challenges for municipal trans- (Cluster 3) are opting for electric buses and policies relating to transportation — policies portation policymaking. However, there is no the poorest, dense cities with low mobili- that could together curtail the rapidly gro- systematic understanding of the unique mo- ty levels (Cluster 4) have policies focused wing demand for personal vehicles in China. torization and urbanization trends of Chinese on road-building to connect urban cores to Based on a mathematical analysis of histori- cities and how physical characteristics map rural areas. Transportation policies among cal data plus text analysis of policy reports, to their transportation policy priorities. The Chinese cities are at least partially reflective the team concludes that Chinese cities that authors adopt a mixed-method approach to of urbanization and motorization trends and have experienced similar urban development address this knowledge gap. They conduct a policy learning needs to account for these and motorization trends over time prioritize time-series clustering of 287 Chinese cities distinct patterns in both physical conditions the same types of transportation policies to using eight indicators of urbanization and- and policy priorities. Their mixed-method deal with their local conditions. Such a pat- motorization from 2001 to 2014, identifying approach (involving time-series clustering tern is of interest to urban decision makers four distinct city clusters. and qualitative policy profiling) provides a seeking role models for developing trans- way for government officials to identify peer portation policies. Moody et al. compile a policy matrix of 21 cities as role models or collaborators in policy types from 44 representative cities forming more targeted, context-specific, and In addition to looking to Beijing and Shanghai and conduct a qualitative comparison of visionary transportation policies. — the trendsetters for innovative policyma- transportation policies across the four city king — decision makers can now learn by clusters. They find clear patterns among po- working with cities that face transportation licies adopted within city clusters and dif- challenges more similar to their own. The ferences across clusters. Wealthy megaci- researchers’ novel methodology combining ties (Cluster 1) are leveraging their existing data and text analysis can be applied in urban rail with multimodal integration and other rapidly developing countries with he- transit-oriented development, while more terogeneous urban areas. car-oriented wealthy cities (Cluster 2) are Transportation policymaking in Chinese cities 4 In recent decades, urban populations in China’s throughout the country. So Moody, Zhao, and Beijing and Shanghai are usually viewed as trend- cities have grown substantially, and rising incomes their team wanted to consider the process in these setters in innovative transportation policymaking, have led to a rapid expansion of car ownership. overlooked cities. In particular, they asked: How and municipal leaders in other Chinese cities turn Indeed, China is now the world’s largest market do municipal leaders decide what transportation to those megacities as role models. for automobiles. The combination of urbanizati- policies to implement, and can they be better en- But is that an effective approach for them? After on and motorization has led to an urgent need for abled to learn from one another’s experiences? all, their urban settings and transportation challen- transportation policies to address urban problems The answers to those questions might provide ges are almost certainly quite different. Wouldn’t such as congestion, air pollution, and greenhouse guidance to municipal decision makers trying to it be better if they looked to “peer” cities with gas emissions. address the different transportation-related chal- which they have more in common? lenges faced by their cities. For the past three years, an MIT team led by Jo- Moody, Zhao, and their DUSP colleagues — anna Moody PhD ’19, research program manager The answers could also help fill a gap in the rese- postdoc Shenhao Wang PhD ’20 and graduate of the MIT Energy Initiative’s Mobility Systems arch literature. The number and diversity of cities students Jungwoo Chun and Xuenan Ni MCP ’19, Center, and Jinhua Zhao PhD ’09, the Edward H. across China has made performing a systematic all in the JTL Urban Mobility Lab — hypothesi- and Joyce Linde Associate Professor in the De- study of urban transportation policy challenging, zed an alternative framework for policy-learning partment of Urban Studies and Planning (DUSP) yet that topic is of increasing importance. In re- in which cities that share common urbanization and director of MIT’s JTL Urban Mobility Lab, sponse to local air pollution and traffic congesti- and motorization histories would share their has been examining transportation policy and on, some Chinese cities are now enacting policies policy knowledge. Similar development of city policy-making in China. “It’s often assumed that to restrict car ownership and use, and those local spaces and travel patterns could lead to the same transportation policy in China is dictated by the policies may ultimately determine whether the un- transportation challenges and therefore to similar national government,” says Zhao. “But we’ve seen precedented growth in nationwide private vehicle needs for transportation policies. that the national government sets targets and then sales will persist in the coming decades. allows individual cities to decide what policies to To test their hypothesis, the researchers needed implement to meet those targets.” Policy learning to address two questions. To start, they needed to know whether Chinese cities have a limited num- Many studies have investigated transportation Transportation policymakers worldwide benefit from a practice called policy-learning: Decision ber of common urbanization and motorization policymaking in China’s megacities like Beijing histories. If they grouped the 287 cities in China and Shanghai, but few have focused on the hund- makers in one city look to other cities to see what policies have and haven’t been effective. In China, based on those histories, would they end up with reds of small- and medium-sized cities located a moderately small number of meaningful groups 5 of peer cities? And second, would the cities in each group have similar transportation policies and priorities? Grouping the cities Cities in China are often grouped into three “tiers” based on political administration, or the types of jurisdictional roles the cities play. Tier 1 includes Beijing, Shanghai, and two other cities that have the same political powers as provinces. Tier 2 in- cludes about 20 provincial capitals. The remaining cities — some 260 of them — all fall into Tier 3. These groupings are not necessarily relevant to the cities’ local urban and transportation conditions. Moody, Zhao, and their colleagues instead wanted to sort the 287 cities based on their urbanizati- on and motorization histories. Fortunately, they had relatively easy access to the data they needed. Every year, the Chinese government requires each city to report well-defined statistics on a variety of measures and to make them public. Among those measures, the researchers chose four indicators of urbanization — gross dome- stic product (GDP) per capita, total urban po- Figure 1: Trajectories of the eight motorization and urbanization indicators used in the clustering analysis. These curves show the aver- age time series trajectories of the four city clusters on the four motorization indicators (top row) and four urbanization indicators (bot- pulation, urban population density, and road area tom row) that the researchers used in their clustering analysis for the 287 Chinese cities. The “Subway length per capita” display shows per capita — and four indicators of motorization data for only Cluster 1 cities; the other three clusters had no subway systems by 2014 so would all appear at zero. Moody et al. 2019: 4. — the number of automobiles, taxis, buses, and 6 subway lines per capita. They compiled those data Cluster 2: 41 wealthy cities that don’t have urban First, they selected 44 cities at random (with the from 2001 to 2014 for each of the 287 cities. rail and therefore are more sprawling, have lower stipulation that at least 10% of the cities in each The next step was to sort the cities into groups population density, and have auto-oriented travel cluster had to be represented). They then down- based on those historical data sets — a task they patterns. loaded the 2017 mayoral report from each of the 44 cities. accomplished using a clustering algorithm. For Cluster 3: 134 medium-wealth cities that have a the algorithm to work well, they needed to select low-density urban form and moderate mobility Those reports highlight the main policy initiatives parameters that would summarize trends in the fairly spread across different modes, with limited and directions of the city in the past year, so they time series data for each indicator in each city. but emerging car use. include all types of policymaking. To identify the They found that they could summarize the 14-year transportation-oriented sections of the reports, Cluster 4: 89 low-income cities that have gene- change in each indicator using the mean value and rally lower levels of mobility, with some public the researchers performed keyword searches on two additional variables: the slope of change over transit buses but not many roads. Because people terms such as transportation, road, car, bus, and time and the rate at which the slope changes (the usually walk, these cities are concentrated in terms public transit. They extracted any sections high- acceleration). of density and development. lighting transportation initiatives and manually labeled each of the text segments with one of 21 Based on those data, the clustering algorithm The figure 1 plot the central trajectories for the examined different possible numbers of grou- policy types. They then created a spreadsheet or- four clusters on each of the eight urbanization ganizing the cities into the four clusters. Finally, pings, and four gave the best outcome. “With four and motorization indicators used in the analysis. groups, the cities were most similar within each they examined the outcome to see whether there For every indicator, there are clear differences in were clear patterns within and across clusters in cluster and most different across the clusters,” the trajectories of the four clusters. says Moody. “Adding more groups gave no addi- terms of the types of policies they prioritize. tional benefit.”. The four groups of similar cities City cluster and policy priorities “We found strikingly clear patterns in the types of are as follows. The researchers’ next task was to determine transportation policies adopted within city clusters and clear differences across clusters,” says Moo- Cluster 1: 23 large, dense, wealthy megacities that whether the cities within a given cluster have have urban rail systems and high overall mobility transportation policy priorities that are similar to dy. “That reinforced our hypothesis that different levels over all modes, including buses, taxis, and each other — and also different from those of motorization and urbanization trajectories would private cars. This cluster encompasses most of the cities in the other clusters. With no quantitative be reflected in very different policy priorities.”. government’s Tier 1 and Tier 2 cities, while the Tier data to analyze, the researchers needed to look for 3 cities are distributed among Clusters 2, 3, and 4. such patterns using a different approach. 7

Figure 2 provides an overview of the cluster, their characteristics and their transportation policy profiles of the year 2017. For a detailed listing and description of the city cluster, see the following pages. Cluster Characteristics Transportation policy priorities in 2017

 Expanding existing urban rail and improving bus services  23 cities, most of the Tier 1 and Tier 2 cities  Improving multimodal connectivity through transfer hubs and nonmotorized transport Cluster  Large, dense, wealthy megacities  Only Cluster to mention transit-oriented development (TOD) 1  Rapid growth of population & GDP and heavy urban rail  Intelligent transport systems and traffic demand management (TDM)  Highest overall mobility levels  Continuing to invest in urban expressways

 41 cities  Developing new urban rail  Low-density, sprawling and wealthy cities  Intelligent transportation systems and TDM Cluster 2  Rapid growth of GDP but not population  Improving and expanding (clean energy) bus service  Auto-oriented pattern of mobility  Continuing to invest in urban expressways  Increasing urban sprawl by significant investment in road infrastructure  Providing public transport discounts to decrease auto-oriented travel

 Emphasizing clean energy (electric) buses  134 medium-wealth cities (the “most common city in China”) Cluster  Improving and expanding bus service  Low-density 3  Continuing significant investment in additional parking spaces as well as in urban  Moderate mobility, limited but emerging car use expressways and rural roads

 89 low-income cities  High density and low-wealth cities (“walking” cities)  Expanding road development to connect the urban core to rural areas on the periphery Cluster 4  Low levels of mobility. Lowest number of buses & taxis with highest  Prioritizing interconnection with other cities in the region by heavy investments in growth in automobiles roads, intercity highways, intercity rails and airports  Lowest levels and growth of road investment

Figure 2: Overview of the Chinese city clusters, their characteristics and their transportation policy priorities. Own illustration based on Moody et al. 2019: 6. Cluster 1 - wealthy dense mega cities with high mobility 8 The cities in Cluster 1 have urban rail systems and are starting to consider policies around them. For example, how can they better connect their rail systems with other transportation modes — for instance, by taking steps to integrate them with buses or with biking and walking infra- structure? How can they plan their land use and urban development to be more transit-oriented, such as by providing mixed-use development around the existing rail network?

Figure 3: Policy priority matrix for the 13 representative cluster 1 cities. Moody et al. 2019: 10. 9

Cluster 1 cities are characterized by high urbaniza- modes of transport. Cluster 1 cities have the hig- every single city from Cluster 1 also mentions tion and motorization trends across all modes and hest mention of public bike share systems and significant investment in new urban expressways, are particularly distinguished by the existence of the prioritization of non-motorized transport. roads, and bridges in their government reports. urban rail systems by 2014 (see Figure 1). Furthermore, Cluster 1 cities are the only cities Laying out the transportation policy profile of a (with the exception of Urumqi in Cluster 2) to subset of these Cluster 1 cities, we see that the- mention transit-oriented development (TOD) se high urbanization and motorization levels are and therefore to recognize the key connection accompanied by active policymaking and huge between transportation and land use. investments across all modes of transportation While there is a clear focus on public transit ex- (see Figure 3). In line with the subway per capita pansion as well as increasing mode share for pu- physical characteristic used in the clustering analy- blic transit and non-motorized transport, almost Figure 4: The city of Nanjing, Jiangsu Province. sis, Cluster 1 cities are the only cities to highlight completed urban rail lines in their city government reports. Furthermore, 12 of the 13 cities highlight planned or ongoing expansion of these existing urban rail systems. Harbin

In addition to massive investment in urban rail, Shenyang every single selected city from Cluster 1 highlights the purchase of new buses, the addition of new bus lines (on dedicated infrastructure), and the Nanjing optimization or increased frequency on current Wuxi Chongqing Suzhou bus routes. Multiple cities use the term “bus me- Chengdu tropolis” to highlight their strategy of expanding bus-based public transit infrastructure in addition Kunming Guangzhou Foshan Dongguan Shenzhen to urban rail lines. They also have a much gre- Zhongshan ater focus on multimodal transfer hubs between rail, bus, and non-motorized or “slow” or “green” Figure 5: Overview of the 13 representative cities of the Chinese city cluster 1 (in total 23). Cluster 2 - wealthy sprawling medium-sized auto-oriented cities 10 Cluster 2 cities are building urban rail systems, but they’re generally not yet thinking about other policies that can come with rail development. They could learn from Cluster 1 cities about other factors to take into account at the outset. For example, they could develop their urban rail with issues of multi-modality and of transit-oriented development in mind.

Figure 6: Policy priority matrix for the nine representative cluster 2 cities. Moody et al. 2019: 11. https://unsplash.com/photos/TZlexBarn-w

Zhuhai1

Macau Photo Agency / unsplah.com

11

Cluster 2 cities are wealthy, medium-sized cities away from existing auto-oriented mobility patterns for urban rail for 2020 and 2030, these cities are that have lower density and more auto-oriented to foster greater public transit mode share. Notab- outliers to the overall trends discussed above for mobility patterns than their Cluster 1 counter- ly, this push for new public transit infrastructure is other Cluster 2 cities. parts. While the presence of subway lines per ca- not complemented by discussion of multimodal pita (by 2014) was a key differentiator of Cluster integration or TOD as seen in Cluster 1. 1 cities from Cluster 2 cities in the clustering ana- Weihai, Karamay, and Daqing do not mention lysis, it is clear that the transportation policy prio- planned or ongoing urban rail construction, in- rities of Cluster 2 cities includes development of stead focusing public transit investment on new new urban rail systems (see Figure 6). and optimized bus routes. While supplemental While no city mentioned completed urban rail searches of additional policy documents sug- lines, most (7 out of 9) highlighted planned or gest that Weihai and Daqing have released plans Figure 7: The city of Zhuhai, Guangdong Province. ongoing urban rail construction in their 2017 city government reports. However, the policy priori- ties of these cities suggest that many are as focu- sed on improving and expanding bus services as Daqing they are on urban rail development. All but one Karamay Urumqi city (Dalian) mentioned new or optimized bus routes. Taken together, this suggests that Cluster 2 cities are focused on improving public transit Dalian Weihai mode share through new infrastructure develop- Jinan Qingdao ment. Interestingly, the only 4 cities in the quali- Changzhou tative policy matrix that mention public transport discounts are all in Cluster 2, suggesting that inf- rastructure investment is being complemented by other policies to improve public transit mode share. Zhuhai Despite continued investment in urban roads, this suggests that Cluster 2 cities are looking to move Figure 8: Overview of the nine representative cities of the Chinese city cluster 2 (in total 41). Cluster 3 - the „most common city“ with moderate mobility and low-density 12 In Cluster 3 cities, policies tend to emphasize electrifying buses and providing improved and expanded bus service. In these cities with no rail networks, the focus is on making buses work better.

Figure 9: Policy priority matrix for the 14 representative cluster 3 cities. Moody et al. 2019: 12. 13

Cluster 3 cities are low-density, medium-wealth ment interests could have significant impact on how government reports, instead Linyi and Mudanji- cities with moderate mobility. This cluster re- the motorization and urbanization in these cities ang focus exclusively on urban and rural road de- presents the largest number of Chinese cities (in continue to develop. Although the within-cluster velopment while Yuxi mentions clean energy cars total 134), which are distinguished by their mo- patterns discussed above are clear, there is also sig- (private electric passenger vehicles) and bike lanes. derate-to-low levels across all urbanization and nificant variation among the representative cities in motorization indicators (see Figure 1). From the Cluster 3. In particular, three cities — Linyi, Yuxi, relative sparseness in Figure 9, we see that these and Mudanjiang — appear to be outliers from the cities only have a moderate focus on transportati- general trend of (clean energy) bus-focused public on in their 2017 city government reports. Unlike transport development in the other Cluster 3 cities. Cluster 1 and Cluster 2 cities, cities in Cluster 3 These cities do not highlight bus investment (in make no mention of either ongoing or planned terms of new routes or new fleets) in their 2017 Figure 10: Aerial photo of the city of Yuxi, Yunnan Province. urban rail construction. Instead, the public transit focus is on expanding and optimizing bus routes. Of all clusters, Cluster 3 cities have the greatest focus on clean energy buses, with 10 out of the 14 representative cities highlighting ongoing or 1 planned procurement of electric buses. 2 3 1 Jiamusi 2 Mudanjiang While the 2017 city government reports in Cluster 4 5 6 3 Tieling 3 highlight clean energy bus systems, they also show 4 Jinzhou 7 5 Anshan competitive investment in car-oriented (rather than 8 9 10 11 6 Dandong public-transit-oriented) infrastructure. Cluster 3 ci- 12 7 Weifang ties have the highest mention of additional parking 13 8 Jining 9 Linyi facilities compared to cities in the other clusters, 10 Rizhao with 10 out of the 14 representative cities referring 11 Lianyungang 14 to recent, ongoing, and/or planned parking space 12 Yangzhou 13 Zigong development. In addition, Cluster 3 cities also men- 14 Yuxi tion the construction of rural and urban roads. The relative focus between these two competing invest- Figure 11: Overview of the 14 representative cities of the Chinese city cluster 3 (in total 134). Cluster 4 - dense „walking“ cities with low levels of mobility and wealth 14 Cluster 4 cities are still focused on road development, even within their urban areas. Policy priorities often emphasize connecting the urban core to rural areas and to adjacent cities — steps that will give their populations access to the region as a whole, expanding the opportunities available to them.

Figure 12: Policy priority matrix for the eight representative cluster 4 cities. Moody et al. 2019: 12. 15

Cluster 4 cities are smaller, lower-income cities While not highlighted in Figure 9, it was also ob- with dense urban cores and relatively low mobility served that cities in Cluster 4 mentioned PPPs as patterns across all modes (see Figure 1). Overall, a potential way to finance new transport and other transportation policy is less of a priority among infrastructure projects more than cities in other these cities compared to cities in other clusters clusters (potentially to supplement their more limi- as evidenced by very few transportation policies ted municipal resources). being highlighted in the city government reports (see Figure 12). While some of these cities (about half) highlight efforts to optimize existing (mixed-traffic) bus routes within the urban core, their transportati- Figure 13: The city of Qujing, Yunnan Province. on policy priorities are much more focused on interconnections with the rural areas on the pe- riphery of their urban core and with other cities in the region. For example, 6 of the 8 cities men- 1 tion construction of significant lengths of rural roads (2500–8000 km in the past 5 years), with most cities planning to construct more. In addi- tion to rural roads, 7 cities mention construction of expressways/highways and 4 mention the de- velopment of intercity rail to help connect the 1 Suihua 2 city with economic opportunities in other cities 4 3 2 Bazhong 5 3 Nanchong and parts of the region. Another key piece of the 6 4 Ya‘an transportation policy profile of these cities is the 8 7 5 Yibin 6 Zhaotong construction of new, domestic airports to help 7 Qujing solidify the city‘s position as a regional transpor- 8 Baoshan tation hub. Figure 14: Overview of the eight representative cities of the Chinese city cluster 4 (in total 89). Annex: Overview of the four clusters and the cities 16

Cluster 1 Cluster 2 Cluster 3 CITY PROVINCE CITY PROVINCE Huaian Jiangsu Nantong Jiangsu CITY PROVINCE CITY PROVINCE CITY PROVINCE Huaibei Anhui Pingdingshan Henan Beijing Beijing Anshan Liaoning Huanggang Hubei Pingxiang Jiangxi Changsha Hunan Benxi Liaoning Anyang Henan Changchun Jilin Huangshan Anhui Puer Yunnan Chengdu Sichuan Changzhou Jiangsu Baicheng Jilin Huangshi Hubei Qingyuan Guangdong Chongqing Chongqing Dalian Liaoning Baishan Jilin Huludao Liaoning Qinhuangdao Hebei Dongguan Guangdong Daqing Heilongjiang Baiyin Gansu Hulunbeier Inner Mongolia Qiqihar Heilongjiang Foshan Guangdong Dongying Shandong Baoding Hebei Huzhou Qitaihe Heilongjiang Guangzhou Guangdong Erdos Inner Mongolia Baoji Shaanxi Jiamusi Heilongjiang Quanzhou Fujian Hangzhou Zhejiang Guiyang Guizhou Bayannaoer Inner Mongolia Jiangmen Guangdong Quzhou Zhejiang Harbin Heilongjiang Haikou Hainan Beihai Guangxi Jiaozuo Henan Rizhao Shandong Kunming Yunnan Hefei Anhui Bengbu Anhui Hohhot Inner Mongolia Jiaxing Zhejiang Shaoxing Zhejiang Nanjing Jiangsu Binzhou Shandong Jieyang Guangdong Shijiazhuang Hebei Ningbo Zhejiang Huainan Anhui Cangzhou Hebei Huizhou Guangdong Jilin Jilin Shiyan Hubei Shanghai Shanghai Jiayuguan Gansu Changzhi Shanxi Jincheng Shanxi Shuangyashan Heilongjiang Shenyang Liaoning Jinan Shandong Chaoyang Liaoning Jingdezhen Jiangxi Shuozhou Shanxi Shenzhen Guangdong Jinchang Gansu Chaozhou Guangdong Jinhua Zhejiang Siping Jilin Suzhou Anhui Karamay Xinjiang Chengde Hebei Jining Shandong Songyuan Jilin Suzhou Jiangsu Laiwu Shandong Chenzhou Hunan Jinzhong Shanxi Suizhou Hubei Tianjin Tianjin Lanzhou Gansu Chifeng Inner Mongolia Jinzhou Liaoning Suqian Jiangsu Wuhan Hubei Lhasa Tibet Chizhou Anhui Jiuquan Gansu Taian Shandong Wuxi Jiangsu Panjin Liaoning Chuzhou Anhui Panzhihua Sichuan Jixi Heilongjiang Taizhou Jiangsu Xian Shaanxi Dandong Liaoning Kaifeng Henan Taizhou Zhejiang Zhengzhou Henan Qingdao Shandong Datong Shanxi Sanya Hainan Langfang Hebei Tianshui Gansu Zhongshan Guangdong Shantou Guangdong Dazhou Sichuan Lianyungang Jiangsu Tieling Liaoning Shizuishan Ningxia Dezhou Shandong Liaocheng Shandong Tongchuan Shaanxi Taiyuan Shanxi Ezhou Hubei Liaoyang Liaoning Tongliao Inner Mongolia Tangshan Hebei Fangchenggang Guangxi Liaoyuan Jilin Weifang Shandong Tongling Anhui Fushun Liaoning Yunnan Wenzhou Zhejiang Urumqi Xinjiang Fuxin Liaoning Linyi Shandong Wuhu Anhui Weihai Shandong Fuzhou Fujian Liuzhou Guangxi Wulanchabu Inner Mongolia Wuhai Inner Mongolia Ganzhou Jiangxi Xiamen Fujian Luohe Henan Wuwei Gansu Guyuan Ningxia Luoyang Henan Wuzhong Ningxia Xining Qinghai Handan Hebei Yichun Heilongjiang Maanshan Anhui Wuzhou Guangxi Yinchuan Ningxia Hebi Henan Maoming Guangdong Xiangtan Hunan Zhoushan Zhejiang Hegang Heilongjiang Mudanjiang Heilongjiang Xiangyang Hubei Zhuhai Guangdong Hengshui Hebei Nanchang Jiangxi Xingtai Hebei Zibo Shandong Hezhou Guangxi Nanning Guangxi Xinxiang Henan 17

CITY PROVINCE Cluster 4 CITY PROVINCE CITY PROVINCE Xinyu Jiangxi Longyan Fujian Yibin Sichuan Xuancheng Anhui CITY PROVINCE Loudi Hunan Yichun Jiangxi Xuchang Henan Ankang Shaanxi Lu‘an Anhui Yingtan Jiangxi Xuzhou Jiangsu Anqing Anhui Luzhou Sichuan Yiyang Hunan Yanan Shaanxi Anshun Guizhou Lüliang Shanxi Yongzhou Hunan Yancheng Jiangsu Baise Guangxi Meishan Sichuan Yueyang Hunan Yangquan Shanxi Baoshan Yunnan Meizhou Guangdong Yulin Guangxi Yangzhou Jiangsu Bazhong Sichuan Mianyang Sichuan Yunfu Guangdong Yantai Shandong Bozhou Anhui Nanchong Sichuan Zhangjiajie Hunan Yichang Hubei Changde Hunan Nanping Fujian Zhangzhou Fujian Yingkou Liaoning Chaohu Anhui Nanyang Henan Zhanjiang Guangdong Yulin Shaanxi Chongzuo Guangxi Neijiang Sichuan Zhaotong Yunnan Yuncheng Shanxi Deyang Sichuan Ningde Fujian Zhoukou Henan Yuxi Yunnan Dingxi Gansu Pingliang Gansu Ziyang Sichuan Zaozhuang Shandong Fuyang Anhui Putian Fujian Zunyi Guizhou Zhangjiakou Hebei Fuzhou Jiangxi Puyang Henan Gansu Guangan Sichuan Qingyang Gansu Zhaoqing Guangdong Guangyuan Sichuan Qinzhou Guangxi Zhenjiang Jiangsu Guigang Guangxi Qujing Yunnan Zhongwei Ningxia Guilin Guangxi Sanmenxia Henan Zhumadian Henan Hanzhong Shaanxi Sanming Fujian Zhuzhou Hunan Hechi Guangxi Shangluo Shaanxi Zigong Sichuan Heihe Heilongjiang Shangqiu Henan Hengyang Hunan Shangrao Jiangxi Heyuan Guangdong Shanwei Guangdong Heze Shandong Shaoguan Guangdong Huaihua Hunan Shaoyang Hunan Jian Jiangxi Suihua Heilongjiang Jingmen Hubei Suining Sichuan Jingzhou Hubei Tonghua Jilin Jiujiang Jiangxi Weinan Shaanxi Laibin Guangxi Xianning Hubei Leshan Sichuan Xianyang Shaanxi Lincang Yunnan Xiaogan Hubei Linfen Shanxi Xinyang Henan Lishui Zhejiang Xinzhou Shanxi Liupanshui Guizhou Yaan Sichuan Longnan Gansu Yangjiang Guangdong Deutsche Gesellschaft für Tayuan Diplomatic Office Building 2-5 Internationale Zusammenarbeit (GIZ) GmbH 14 Liangmahe South Street, Chaoyang District 100600 Beijing, P. R. China Registered offices T +86 (0)10 8527 5589 Bonn und Eschborn, Germany F +86 (0)10 8527 5591 E [email protected] I www.sustainabletransport.org I www.giz.de