Emissions and low-carbon development in Guangdong-Hong Kong-Macao Greater Bay Area cities and their surroundings
Graphical abstract
Introduction
According to latest statistics of the International Energy Agency (IEA), global energy demand rose by 2% in 2017, while 72% of the demand growth was met by oil, natural gas and coal. Meanwhile, the energy-related CO2 emission grew by 1.4% and reached a historic high of 32.5 gigatonnes (Gt) IEA [1]. Such huge amounts of fossil fuel-related CO2 emission have forced decision makers to implement climate change mitigation actions and achieve sustainable development. As centres of commerce and industry of regions or countries, cities are the major contributors to energy consumption and CO2 emissions [2], [3], [4]. In recent decades, with rapid population transfer as well as accelerating urbanisation processes, the cities’ energy demand and human-induced CO2 emissions have experienced continuous increases [1], [5]. Urban contributed more than 70% of global CO2 emissions as well as two-thirds of global energy consumption [6]. Meanwhile, cities are regarded as the leading innovators and implementers of policy measures in climate change mitigation and low-carbon development [7], [8], [9]. Bay area comprises scores of cities surrounding the bay, featuring developed service industries, major global financial centres and transportation junctions. With the clustering impact of urban agglomeration, bay area undertakes diverse (i.e. technological, institutional, industrial and financial) innovation activities and increasingly exerts an active role in global economic development. Bay area is regarded as key player in the transition to sustainable energy system and mitigation of rapid climate change impacts [10]. It is therefore essential that more endeavours be taken to play leadership roles of urban agglomeration in CO2 emission mitigation.
Establishing accurate emission inventories and understanding the cities’ emission characteristics is the first step to conduct climate change mitigation and adaption actions at a city-level. Emission inventories are regarded as important tools for authorities in CO2 emission mitigation policy formulation and implementation [11], [12], [13], [14], [15], [16], [17]. City-level CO2 emission inventories were compiled by applying bottom-up and top-down approaches from spatial and temporal perspectives [18], [19], [20], [21], [22], [23], [24]. For example, Dhakal [25] selected 35 key Chinese provincial capitals/cities and calculated total energy consumption, CO2 emissions and average carbon intensity of these cities. He found that these cities consumed 40% of the total commercial energy of nation and emitted CO2 at similar levels. Dodman [11] produced urban greenhouse gas emission inventories by city and sector, while the main drivers for high levels of greenhouse gas production were identified, and the role and potential for cities to reduce global greenhouse gas emissions were examined. Brondfield [19] modelled two on-road CO2 emission inventories scaling approach and applied it to Boston (USA) for capturing urban road-type spatial heterogeneity. Liu, Liang [26] complied top-down energy-related greenhouse gas emission inventories of the four Chinese mega-cities during 1995–2009. Sugar, Kennedy [20] provided comprehensive and detailed emissions inventories for Shanghai, Beijing, and Tianjin in 2006, compared to ten other global cities and discussed the issues concerning low-carbon growth in China. Kennedy [21] calculated the greenhouse inventories of global 22 cities using a bottom-up approach under the United Framework Convention on Climate Change (UNFCC), variations of emission mitigation strategies result from the differences in cities characteristics were discussed. Xu, Huo [27] estimated the CO2 emissions derived from the fossil fuel combustion and industrial processes of 18 central cities in China between 2000 and 2014. Shan [9] investigated the CO2 emissions of 182 cities in China and explored their emission reduction capacities. Markolf, Matthews [24] calculated production-based GHG emissions for the 100 most populated metropolitan areas in the United States in 2014 based on national datasets. Wang and Liu [28] assessed city-level CO2 emission based on DMSP/OLS ‘city lights’ satellite data. Generally, the bottom-up approach estimated emissions resulting from local energy activities and technologies at the site location based on spatial and geographical techniques and/or night-light data. However, data collection and accounting procedures differ from site to site, and from person to person, compiled inventories based on different sources, scope and sector sets were not consistent and comparable [4]. In comparison, the top-down approach mainly focused on economic modelling (i.e., computable general equilibrium, input-output method), emissions were calculated at the headquarters level. It may lead to errors when aggregated data do not accurately reflect location conditions [16]. Moreover, most of the previous city-level emission inventories were not accorded with national/provincial inventories and cannot be re-use and cross-validated.
As one of China’s current national key economic development strategies, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was first highlighted in the 13th Five-Year Plan (2016–2020) in 2016. The new initiative is an updated version of previous regional development initiatives, such as the Pearl River Delta and the Pan-Pearl River Delta [29]. GBA consists of cities of “9 + 2”, that is nine cities in Guangdong Province (Guangzhou, Shenzhen, Zhuhai, Dongguan, Huizhou, Zhongshan, Foshan, Zhaoqing and Jiangmen from Pearl River Delta), as well as two Special Administrative Regions (Hong Kong and Macao). Local authorities have implemented a series of low-carbon policies and each city has their own specific development goal in GBA development plan (as shown in Table 1) [30]. The detailed rules and related policies of GBA should be released in 2018 [31]. GBA covers less than one percent of the country’s land area, it created 13% of the national Gross Domestic Product (GDP) in 2016 with only 5% of total population [32]. With competitive key industries on the global stage, such as manufacturing (high-tech), transportation (sea and air cargo services), trade-related services (sourcing, trading, freight-forwarding, finance) as well as the digital and innovation industry, GBA urban agglomeration intends to highlight the region’s role and aspiration in the global economic supply chain. In addition, the GBA serves as a major international land and maritime corridor connecting countries along the Silk Road Economic Belt (Central Asia and Europe) and Maritime Silk Road (South Asia, Oceania to Africa and the Middle East) to boost global trade liberalization. The GBA plays significant roles in promoting global low-carbon, sustainable and coordinated development. CO2 emission inventories are a foundation for identifying the sectors, sources and activities responsible for emissions, generating GBA low carbon development strategies, and monitoring progress towards the climate change mitigation policy goal. However, seldom studies focused on air pollutant emissions from San Francisco Bay Area [33], [34]. Few studies have discussed the emission inventories and characteristics of GBA cities.
Therefore, in order to fill the gap, for the first time, we conduct time-series CO2 emission inventories of 11 GBA cities and 12 surroundings cities based on IPCC territorial emission accounting approach. Our inventories are constructed by 17 fossil fuels, 47 socio-economic sectors, and 7 industrial processes, which are consistent and comparable with the national and provincial inventories. We provide all the primary data and results on www.ceads.net for freely download, making the emission data transparent, verifiable, and re-usable for the academic society and policy stakeholders. At last, the integrated socio-economic-emission analysis of GBA and their surroundings could be conducted. Overall low-carbon roadmaps for the cities are figured based on current emission characteristic, urbanisation process, industrial maturity and resource availability, and provide a benchmark for other urban agglomeration as well as cities along the Belt and Road.
Section snippets
Methods and data sources
In this study, based on sectoral approach of the Intergovernmental Panel on Climate Change (IPCC) method, administrative territorial-based CO2 emissions of GBA cities are calculated from production side [35]. According to Shan, Guan [4], CO2 emissions emitted by 17 kinds of fossil fuel consumption and 7 industrial processes within the city boundary are considered (see Tables S1 and S2 in the Supporting Information). The emissions from electricity generation are calculated with primary energy
CO2 emissions of GBA cities and their surroundings
By calculating 21 Guangdong cities and incorporating Hong Kong and Macao, CO2 emission inventories of GBA and their surroundings from 2000 to 2016 have been complied (see Table 3). Total emissions of 23 cities increased from 426 Mt in 2000 to 610 Mt in 2016. Guangzhou, Shaoguan, Hong Kong are top three emitters and respectively produced 66 (12%), 52.3 (9%) and 47 (8%) million tonnes CO2, equal to a quarter of total emissions. GBA cities contributed 16.4% higher emissions than the surrounding
Conclusions and policy recommendations
As the one of the China’s current key economic development strategy, Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is expected to be the world’s largest bay area and plays significant roles in promoting world’s low-carbon and sustainable development, as well as the Belt and Road initiative. In this study, this is the first attempt to compile CO2 emission inventory of 11 GBA cities and their surroundings based on IPCC territorial emission accounting approach, which are consistent and
Acknowledgements
All the data and results can be download freely from China Emission Accounts and Datasets (CEADs) at http://www.ceads.net. This work was supported by the Natural Science Foundation of China (71704029), Humanities and Social Science Foundation in Ministry of Education of China (16YJCZH162), Guangzhou Planning Project of Social Science (2016GZQN25), and the New Pearl River Star Program of Guangzhou City (201610010035).
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