Spatiotemporal decomposition analysis of carbon emissions on Chinese residential central heating
Introduction
China has the highest carbon emissions globally, and therefore, the country’s emission mitigation actions are significant to mitigate climate change and maintain the sustainable development of human society [1]. As a responsible country, China acknowledged that its carbon emissions will peak in 2030 and carbon emissions per unit of gross domestic product (GDP) in 2030 will decrease by 60–65% compared to 2015 [2]. Furthermore, during the 75th session of the United Nations General Assembly (UNGA), China announced an ambitious emission target: to be carbon neutral by 2060. Notably, one of the major challenges for China, a developing country, is to achieve the carbon emission mitigation while meeting the growing demands of its residents [3].
There is a substantial demand for heating in northern China due to the extremely cold and long winter duration [4]. Therefore, China has adopted a central heating policy in the area north of the Qinling Mountains and Huaihe River; this is one of the most important policies for people's livelihood in China [5]. Central heating is considered to be one of the dominant causes of emissions and energy consumption in China [6], and carbon emission mitigation in central heating is important for achieving the carbon neutral target. In the past decades, China's energy consumption and emissions from residential central heating (RCH) have increased significantly due to rapid urbanization and population growth [4]. According to the China Association of Building Energy Efficiency (CABEE) [7], the carbon emission of RCH accounts for approximately 28% of a residential building's carbon emissions. Additionally, the high area-related energy intensity of RCH in China, which is 2–3 times that of developed countries, is often criticized.
RCH in China is generated by burning fossil fuels, especially coal, which, in 2015, accounted for more than 90% of total energy structure [3]. Thus, RCH results in high carbon emissions, and generates a variety of toxic and harmful substances [8], such as sulfur dioxide [9] and PM2.5. Central heating poses a serious threat to China's environment and the health of its residents [5]. Studies have confirmed that current central heating policies have increased the death rate from heart and lung diseases and reduced life expectancy of residents living in northern China [8], [10], [11], [12]. Therefore, the Chinese government proposed a series of strict policies and pollution control plans to limit the negative impacts of RCH. For example, in 2017, China Ministry of Environmental Protection (CMEE) proposed the “2 + 26” Cities Interim Policy on Urban Air Pollution Control [13], which mandates rigorous standards on boilers in China's Beijing–Tianjin–Hebei region. And this policy implementing the “coal to electricity” and “coal to gas” boiler renewal policies and prohibiting the use of medium and small boilers [13]. In the same year, the National Development and Reform Commission (NDRC) and nine other departments released the “Clean Heating Plan for Northern Regions in Winter” (2017–2021), the plan mandated clean heating rates of 60% and 89% in Chinese cities to be in 2019 and 2021, respectively [14]. The country has a heating mode dominated by coal-fired and cogeneration boilers; however, the use of gas-fired boilers is increasing continuously [3].
Currently, China is in a critical period of heating reform and development. To formulate rational policies and realize the emission mitigation goals of the heating industry in the future, it is important for China to clarify its historical emission trends and major driving factors of RCH carbon emissions and understand the main issues regarding heating emission mitigation in different regions. Therefore, in this study, we addressed the following three questions:
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What was the RCH trend in China in recent decades?
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What are the main driving factors of RCH at the national and provincial levels?
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How to develop a rational RCH policy of different provinces and accelerate RCH emission mitigation?
To answer these questions, our study first quantified the provincial-level CO2 emissions from RCH for the period 2000–2017. Second, the effects of driving factors on central heating emissions were analyzed by combining the logarithmic mean Divisia index (LMDI) with an extended Kaya identity. Finally, using a spatial LMDI decomposition method, the main interprovincial differences of RCH were analyzed and corresponding emission mitigation strategies were proposed.
This paper is structured as follows: Section 2 conducts a literature review on central heating and decomposition methods. Section 3 introduces the method used to quantify carbon emissions from RCH, develops the RCH emission model using the Kaya identity, and introduces the main calculation flow of the spatiotemporal LMDI model. Section 4 presents and analyzes the results of the models. Section 5 discusses and recommends the main emission mitigation strategies for each province. Finally, Section 6 describes the main findings and implications of our study, along with recommendations for directions of future research.
Section snippets
Studies on central heating
Considering the associated high energy consumption and serious pollution, extensive studies have been conducted on heating. With respect to the quantification of carbon emissions from central heating [15], using data from Yearbook, Du et al. [3] quantified the CO2 emission in China from the central heating supply system for the period 2006–2015, along with quantifying the proportion distribution of coal-fired, cogeneration, and gas-fired boilers at the provincial level. Zhang et al. [4] and Cui
Quantification of Carbon Emissions from Central Heating
The carbon emissions from residential central heating were calculated using the following formula:where Eheating represents the heating power (data obtained from the China Urban and Rural Construction Statistical Yearbook, CURCSY) and EFheating represents the emission factor of heating power. EFheating was calculated using the following equation:where represents the consumption of fuel i used to product heating power, ′ represents the data from
National level carbon emissions residential central heating (RCH)
In Fig. 1a, we can observe that during the study period, although the area-related carbon intensity (CI) continuously decreased from 124.97 kgCO2/m2 to 49.61 kg CO2/m2, the carbon emissions from RCH increased to 321.75 MtCO2 and reached the inflection point in 2016. Furthermore, the emission factor of RCH (represented by a converse-U shape) peaked in 2007 (3.6 kgCO2/kgce) and was 3.40 kgCO2/kgce in 2017.
These results were mainly attributed to three reasons: 1) Improvement in boiler efficiency
Impact of climate parameter on residential central heating (RCH)
The most important hypothesis of this study is that climate parameter has a significant impact on the inter-province EI differences in RCH. To confirm this hypothesis, we selected the coefficient of variation (CV, standard deviation/mean), which can eliminate dimensional effects, to test the inter-province EI differences in RCH. As shown in Table 1, in the past years, the CV of EI has obviously decreased from 0.599 in 2005 to 0.332 in 2015, indicating the inter-province EI differences in RCH
Main conclusions
We quantified the historic carbon emission trend of RCH and analyzed the driving factors of RCH carbon emissions and interprovincial EI differences using spatiotemporal decomposition. Furthermore, by combining the study results with the performance of different parameters in each province, we have suggested a few future clean heating strategies to mitigate future RCH emissions. The main results are as follows:
RCH carbon emissions reached an inflection point of 321.75 MtCO2 in 2016. Although the
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors would like to acknowledge financial support provided by Fundamental Research Funds for the Central Universities (No. 2020CDJSK03XK15), the National Social Science Fund of China (19BJY065), and Graduate research and innovation foundation of Chongqing, China (CYS18052). The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions.
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