Spatiotemporal Characteristics,Determinants and Scenario Analysis of CO2 Emissions in China Using Provincial Panel Data

This paper empirically investigated the spatiotemporal variations, influencing factors and future emission trends of China’s CO₂ emissions based on a provincial panel data set. A series of panel econometric models were used taking the period 1995–2011 into consideration. The results indicated that CO₂ emissions in China increased over time, and were characterized by noticeable regional discrepancies; in addition, CO₂ emissions also exhibited properties of spatial dependence and convergence. Factors such as population scale, economic level and urbanization level exerted a positive influence on CO₂ emissions. Conversely, energy intensity was identified as having a negative influence on CO₂ emissions. In addition, the significance of the relationship between CO₂ emissions and the four variables varied across the provinces based on their scale of economic development. Scenario simulations further showed that the scenario of middle economic growth, middle population increase, low urbanization growth, and high technology improvement (here referred to as Scenario BTU), constitutes the best development model for China to realize the future sustainable development. Based on these empirical findings, we also provide a number of policy recommendations with respect to the future mitigation of CO₂ emissions.     

Environmental regulation and CO2 emissions: Based on strategic interaction of environmental governance

In order to reduce carbon emissions and improve environmental governance, the paper discusses the interactive forms of environmental regulation based on the two-regime spatial Durbin model. The effects of environmental regulation and interactive behavior of environmental regulation on carbon dioxide (CO₂) emissions are explored by using the spatial lag of X (SLX) model. It is found that there is a diversified competitive behavior in the enforcement of environmental regulation among local governments in China. And the diversified competitive behavior results in the nearby transfer of pollution, which increases local CO₂ emissions. In addition, there is an inverted "U" curve between environmental regulation and CO₂ emissions, and China is still in the "green paradox" stage. Furthermore, it is found that the environmental regulation mainly affects CO₂ emissions through industrial structure and technological progress. Also, there are differences in the spatial spillover effect of environmental regulation due to the existence of regional heterogeneity, and its impact on CO₂ is particularly significant in the western region. The findings indicate that the central government should strengthen targeted supervision and adaptive incentives for local governments to implement environmental regulation so that joint emission reduction can be promoted.     

CO2, economic growth,and energy consumption in China's provinces: Investigating the spatiotemporal and econometric characteristics of China's CO2 emissions

This study addresses the spatiotemporal variations at play in China's CO₂ emissions, based on an estimation of emission levels in the period 1995–2012 and an provincial analysis of the relationship of CO₂ emissions to economic growth and energy consumption. Using a series of econometric models and data on the combustion of fossil fuels and cement manufacturing, the study first estimated CO₂ emission levels during the study period, exploring their spatiotemporal pattern. The results indicate that both China's total and its per capita CO₂ emissions have increased significantly over the study period, with both measures evidencing a similar evolution (albeit one that is characterized by noticeable regional discrepancies at the provincial level and which displays properties of convergence). From a geographical perspective, we found both total and per capita CO₂ emissionsto be higher in China's eastern region than in the country's central and western regions. Panel data analysis was subsequently undertaken in order to quantify the dynamic casual relationship between economic growth, energy consumption, and CO₂ emissions. The empirical results indicated that the variables were in fact cointegrated and exhibited a long-run positive relationship. The results of further Granger causality tests indicated the existence of a bidirectional positive causality between economic growth and energy consumption, as well as between energy consumption and CO₂ emissions, and a unidirectional positive causality running from economic growth to CO₂ emissions. The findings of this study suggest that China is, in the long run, dependent on carbon energy consumption for its rapid economic growth, a dependency which is the cause of considerable increases in CO₂ emissions. China should therefore make greater efforts to develop low-carbon technologies and renewable energy, and improve energy efficiency in order to reduce emissions and achieve green economic growth.     

Inter-regional linkage analysis of industrial CO2 emissions in China: An application of a hypothetical extraction method

As the largest CO₂ emitter in the world, China has faced great pressure to mitigate its CO₂ emissions. Thus, issues related to CO₂ emissions in China have been widely studied. However, the industrial linkages of CO₂ emissions at the regional level have been less concerned. This study integrates hypothetical extraction method with the multi-regional input–output model, and investigates industrial CO₂ emission linkages of China at the regional level. Based on the data of China in 2007, which decomposes China into eight regions, this study first analyzes the production-based emission (PBE) and consumption-based emission (CBE) of each region. The PBE and CBE of 10 branches are then analyzed and decomposed into three parts. Finally, this study decomposes the externally produced embodied emission (EPEE) and internally produced embodied emission (IPEE) of Petrochemicals and Minerals in the East Coast, to illustrate how these two indicators of a branch affect other branches in the economy. Results show that regions with large PBE, such as coastal regions and Central, usually have large CBE, whereas Jing-jin has the smallest PBE and CBE. Branches such as Mining and Petrochemicals and Minerals, have large PBE and are net CO₂ emission exporters; while Construction and Other Services are net importers. According to the decomposition results of PBE and CBE, branches can be classified into four groups. The decomposed IPEE and EPEE of Petrochemicals and Minerals in the East Coast show that from the perspective of regions, CO₂ emissions this branch exports to and imports from East Coast are most. From the perspective of branches, decomposition of IPEE shows that Petrochemicals and Minerals in the East Coast exports a large amount of CO₂ emissions to Construction, while the decomposition results of EPEE show that the studied branch imports least CO₂ emissions from Construction. Policy implications deduced from this study are discussed.     

中国能源消费碳排放的时空特征

选择联合国政府间气候变化专门委员会(IPCC)的部门方法和8大类能源,采用1990年至2009年的中国能源统计数据,按照自下而上的思路,对我国各省区的碳排放量进行估算,并从碳排放量、碳排放强度、人均碳排放量和碳排放密指标出发,深入分析了各省区碳排放的时空特征差异。以期对国内碳排放的时空特征分析,有助于决策者和能源分析家提高节能减排政策制定的有效性。     

CO2 emissions and urbanization correlation in China based on threshold analysis

Urbanization and CO₂ emissions trends are driven by worldwide economic development. Studies indicate a direct correlation between urbanization and CO₂ emissions increases with both stimulating and inhibiting factors exhibited in the urbanization process and with periodic and regional characteristics exhibited with CO₂ emission. The relationship between urbanization and CO₂ emissions specifically in China from 1979 to 2013 is researched in this paper The novel points of this paper lies in utilizing a threshold mode to test periodic characteristics and analysis by regions. Results verify the effect of urbanization on CO₂ emissions as: (1) emissions increase when threshold point 0.43 was surpassed; (2) emissions increase as residential income increases; (3) coefficients of urbanization on emissions increase initially and then decrease as a factor of increasing industry percentage in overall GDP; (4) patterns of threshold points vary geographically.     

Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis

As a factor associated with urban management and planning, urban development intensity (UDI) could in fact form the basis for a new rationale in coordinating urban sustainable development and reducing CO₂ emissions. However, existing literature engaging in the task of quantifying the impacts of urban development intensity on CO₂ emissions is limited. Therefore, the goal of this study is to quantify the relationship between urban development intensity and CO₂ emissions for a panel made up of the five major cities in China (Beijing, Shanghai, Tianjin, Chongqing and Guangzhou) using time series data from 1995 to 2011. Firstly, this study calculated CO₂ emissions for the five selected cities and presented a comprehensive index system for the assessment of the level of urban development intensity based on six aspects (land-use intensity, economic intensity, population intensity, infrastructure intensity, public service intensity and eco-environmental intensity) using locally important socioeconomic variables. Panel data analysis was subsequently utilised in order to quantify the relationships between urban development intensity and CO₂ emissions. The empirical results of the study indicate that factors such as land-use intensity, economic intensity, population intensity, infrastructure intensity and public service intensity exert a positive influence on CO₂ emissions. Further, the estimated coefficients suggest that land-use intensity is the most important factor in relation to CO₂ emissions. Conversely, eco-environmental intensity was identified as having a major inhibitory effect on CO₂ emission levels. The findings of this study hold important implications for both academics and practitioners, indicating that, on the path towards developing low-carbon cities in China, the effects of urban development intensity must be taken into consideration.     

中国农业系统近40年温室气体排放核算

基于排放因子法构建了包含种植业和牲畜养殖业的农业系统温室气体排放核算体系,系统核算了1980-2020年我国全国尺度上的农业系统温室气体排放总量和变化趋势,并在区县级尺度下对1980、2000、2011年的中国农业系统的温室气体排放量进行核算,对比不同阶段农业系统温室气体排放变化的时空异质性规律。研究发现：1980-2020年我国农业系统温室气体排放量呈波动增长趋势,增长了近46%。CH₄是农业系统排放贡献最大的温室气体,占总排放量的47.33%。我国农业系统温室气体排放与不同地区农业生产方式有关,CH₄排放量高的地区主要位于我国主要水稻产区以及旱地作物产区。CO₂排放量高的地区主要位于东北、西北等地区以及华东地区。N₂O排放量较高地区主要位于西北的主要畜牧养殖地区,以及我国农业经济发展水平高的中南部地区。研究有助于揭示我国农业温室气体排放的动态特征,现状规律,以及空间差异性特征,从农业减排角度为实现双碳目标提供科学参考。     

Impacts of energy-related CO2 emissions: Evidence from under developed,developing and highly developed regions in China

A large accumulation of carbon dioxide and other greenhouse gases have caused great concern around the world. A great deal of general literature focus on the impact factors of CO₂ emissions at the national, regional and city levels. However, there is little specific guidance on regional difference in CO₂ emissions. In this paper, 30 provincial-level administrative units of China are divided into three different levels of economic development regions according to the GDP per capita from 1997 to 2012. A STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model is used to examine the impact factors on energy-related CO₂ emissions, including population, economic level, technology level, urbanization level, industrialization level and foreign trade degree. The results indicate that the effect of energy intensity is the greatest in highly developed region. Nevertheless, the impact of urbanization, industry structure and foreign trade degree in under developed region is higher than the other two regions. Population and GDP per capita have greater effect on carbon emissions in developing region than the others. Finally, differentiated measures for CO₂ reductions should be adopted according to local conditions of different regions.     

Does energy intensity contribute to CO2 emissions? A trivariate analysis in selected African countries

The present study investigates the dynamic relationship between energy intensity and CO₂ emissions by incorporating economic growth in environment CO₂ emissions function using data of Sub Saharan African countries. For this purpose, we applied panel cointegration to examine the long run relationship between the series. We employed the VECM Granger causality to test the direction of causality amid the variables.At panel level, our results validate the existence of cointegration among the series. The long run panel results show that energy intensity has positive and statistically significant impact on CO₂ emissions. There is also positive and negative link of non-linear and linear terms of real GDP per capita with CO₂ emissions supporting the presence of environmental Kuznets curve (EKC). The causality analysis reveals the bidirectional causality between economic growth and CO₂ emissions while energy intensity Granger causes economic growth and hence CO₂ emissions, while across the individual countries, the results differ. This paper opens up new insights for policy makers to design comprehensive economic, energy and environmental policy for sustainable long run economic growth.     

Life cycle of CO2-emissions from electric vehicles and gasoline vehicles utilizing a process-relational model

This article aims at estimating life cycle CO₂ emissions from electric vehicles (EV) and gasoline vehicles (GV), although the estimation in this study is not an LCA according to ISO14040s. For this purpose, a mathematical tool called the Process-relational model was developed. The Process-relational model is used for establishing life cycle inventories. The model has a structure which improved the principle of input-output analysis in econometrics that only one product is generated by one process. This model enabled us to overcome difficulties of LCA in retracing complicated repercussions among production systems. Then, life cycle CO₂, emissions from electric vehicles (EV) and gasoline vehicles (GV) were estimated with this model. Estimated results indicated that the manufacture and driving of EV resulted in less CO₂ emissions than chose of GV. However, the difference between EV and GV dramatically changed depending on traffic situations. Namely, the difference became larger as the average velocity of the vehicles became lower. We also compared CO₂, emission from manufacturing EV with that from driving EV. The share of manufacture was shown to increase in total CO₂, emissions as the average velocity of the EV became higher. In conclusion, we clarified the direction of research and development of EV and GV for reducing the life cycle CO₂.     

北京城郊地区二氧化碳通量特征

利用位于北京市顺义气象局45 m气象塔上36 m高度的湍流观测资料,对该区域2008年11月1日至2009年10月31日共365d的二氧化碳通量(CO2)的时间变化和各方位的分布特征进行了分析研究,并计算了CO2年排放量。结果表明,CO2受交通因素和居民日常生活排放的影响较小,冬季耗能取暖会显著增加CO2的排放量;受供暖排放和植物生长季节光合作用的影响,冬季的CO2通量值在全天绝大多数时刻均高于其他季节,其日平均值为15.6μmol m-2s-1,显著高于春、夏、秋季的日平均值5.6、5.7和8.8μmol m-2s-1(t-test,P0.001)。各方向CO2通量值的大小与其源区内土地利用/覆盖方式以及建筑物的使用功能和使用性质密切相关,住宅楼、饭店、工厂、旅馆等人工建筑面积占比例越大,CO2排放量越大;而植被覆盖比例较高的方向CO2值较小。观测点周边区域是CO2的排放源,且年平均排放量达到13.6 kg m-2a-1,但低于同一时期北京市内高密度住宅区域的CO2年排放量。     

Estimating quantity and equity of carbon emission from roads based on an improved LCA approach: the case of China

Purpose

Carbon emission from roads is an important contributor of a nation’s greenhouse gas emission that causes climate change. However, the existing life cycle assessment (LCA) analysis of road carbon emissions focus on project-level, ignoring regional differences. Significant challenges remain in developing regional road’s carbon emission mitigation strategies. This study estimates the quantity of carbon emissions from roads in China and calculated the regional equity of road carbon emissions.

Methods

An improved LCA approach, which considered the regional difference of raw materials’ carbon emissions, carbon emissions caused by traffic jam and road category, was applied to calculate the quantity of carbon emissions of roads. Sensitive analysis was conducted to find the key influential factors. Gini coefficient was used to calculate the equity degree of carbon emissions by roads based on the LCA results. The decomposition model of Gini coefficient is applied to analyze the causes of carbon emission differences.

Results

The total national carbon emissions by roads in 2019 increased by 2.2 times compared to 2009. Carbon emission from roads in the operation phase increased from 62% in 2009 to 83% in 2019. The functional unit for expressway in this study ranging from 1646 to 1794 t CO₂e/km in 31 provinces. An estimated uncertainty of plus or minus 4% of the traffic flow allocation between expressway and other roads makes an increase of 38% or a decrease of 15% of the life cycle emission. The overall Gini coefficient of carbon emissions from roads in China is under the warning line of 0.4. Outer inequity between regions contributes 88.83% of the whole inequity and the most developed three regions contribute 66.23%.

Conclusions

Large quantity of road construction in the past in China makes the burden of carbon emission transfer from the construction phase to the operation phase. Regional differences of raw materials’ carbon emissions, traffic jam, and road hierarchy are important factors influencing the LCA-based estimation of road carbon emission. To improve the national equity degree of road carbon emission, quota allocation of road carbon emission rights between regions and cross-regional carbon emission reduction policies would help.

     

旅游交通碳排放的空间结构与情景分析

旅游业作为全球第一大产业,是影响气候变化的重要因素之一,旅游碳排放的相关研究近年来已经引起学者们的关注.选择了九寨沟风景区、西安碑林博物馆、南京珍珠泉风景区3个旅游交通模式差异明显的案例地为例,根据实地问卷调查数据估算了九寨沟风景区、西安碑林博物馆、珍珠泉旅游风景区2010年的旅游交通碳排放总量分别为654.18,108.44和15.92 Gg.通过比较九寨沟、西安碑林和珍珠泉的碳排放累积曲线,得出不同旅游平均距离的景区的碳排放结构均衡度有所不同,旅游平均距离偏低景区的碳排放结构最不均衡.同时,旅游景区的交通碳排放在距离上具有分段性,不同旅游平均距离的景区交通碳排放的空间结构具有明显的差异性.通过4种不同的交通情景分析发现,旅游平均距离高和距离中等的景区对飞机的碳减排敏感度较高,旅游平均距离偏低的景区自驾车的碳减排效果最为明显.研究结果为旅游管理部门根据碳排放结构有针对性的制定差异化的旅游交通碳减排政策提供了参考和借鉴.     

Relationships among carbon emissions,economic growth,energy consumption and population growth: Testing Environmental Kuznets Curve hypothesis for Brazil,China, India and Indonesia

This study examines the impacts of income, energy consumption and population growth on CO₂ emissions by employing an annual time series data for the period 1970–2012 for India, Indonesia, China, and Brazil. The study used the Autoregressive Distributed Lag (ARDL) bounds test approach considering both the linear and non-linear assumptions for related time series data for the top CO₂ emitter emerging countries in both the short run and long run. The results show that CO₂ emissions have increased statistically significantly with increases in income and energy consumption in all four countries. While the relationship between CO₂ emissions and population growth was found to be statistically significant for India and Brazil, it has been statistically insignificant for China and Indonesia in both the short run and long run. Also, empirical observations from the testing of environmental Kuznets curve (EKC) hypothesis imply that in the cases of Brazil, China and Indonesia, CO₂ emissions will decrease over the time when income increases. So based on the EKC findings, it can be argued that these three countries should not take any actions or policies, which might have conservative impacts on income, in order to reduce their CO₂ emissions. But in the case of India, where CO₂ emissions and income were found to have a positive relationship, an increase in income over the time will not reduce CO₂ emissions in the country.     

Identify sectors’ role on the embedded CO2 transfer networks through China’s regional trade

This study developed a framework for combining multi-regional input-output analysis and network indicators to assess the interregional CO₂ flows in China. The interregional CO₂ flows of eight regions were calculated and visualized based on a multiregional input-output (MRIO) model for China. The focus of the research was intermediate use. The results of the network indicators showed that refined petroleum, coke, nuclear fuel and chemical products (07), and basic metals and fabricated metal sectors (09) played key roles in the complex networks. and these sectors in most regions controlled a large share of CO₂ transfer by functioning as key hubs and authorities. They along with commerce, transport, storage, and post (16) acted as agents that brokered the CO₂ flows within and between regions. The roles of some other industrial sectors were also identified, e.g., construction (15) functioned as the largest authority. The results demonstrated the importance and effectiveness of network indicators for identifying the characteristics of CO₂ emissions embedded in the domestic supply chain, and provided new information relevant to policy implementation.     

Examining the impact factors of urban residential energy consumption and CO2 emissions in China – Evidence from city-level data

Rapid urbanization has exerted substantial pressure on China’s energy system and contributed to climate change. To find the key drivers of urban residential energy consumption and CO₂ emissions, this paper uses an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model that employs city-level data to examine the influences of population scale, income level, population compactness and price on house-based residential energy consumption, energy-related CO₂ emissions and private vehicle ownership. The empirical results indicate that factors such as population scale, affluence, and population compactness can lead to increases in residential energy consumption and CO₂ emissions. In terms of transportation, income and population scale positively drive the growth of private vehicle ownership, while the fuel price negatively influences private vehicle ownership. Moreover, population scale is the most important factor in residential energy consumption and CO₂ emissions. Finally, policy recommendations are suggested for China’s urban development strategy and urban design and to encourage technology innovations that reduce residential energy consumption and CO₂ emissions.     

Satellite‐derived estimations of spatial and seasonal variation in tropospheric carbon dioxide mass over China

China has frequently been questioned about the data transparency and accuracy of its energy and emission statistics. Satellite‐derived remote sensing data potentially provide a useful tool to study the variation in carbon dioxide (CO₂) mass over areas of the earth's surface. In this study, Greenhouse gases Observing SATellite (GOSAT) tropospheric CO₂ concentration data and NCEP/NCAR reanalysis tropopause data were integrated to obtain estimates of tropospheric CO₂ mass variations over the surface of China. These variations were mapped to show seasonal and spatial patterns with reference to China's provincial areas. The estimates of provincial tropospheric CO₂ were related to statistical estimates of CO₂ emissions for the provinces and considered with reference to provincial populations and gross regional products (GRP). Tropospheric CO₂ masses for the Chinese provinces ranged from 53 ± 1 to 14,470 ± 63 million tonnes were greater for western than for eastern provinces and were primarily a function of provincial land area. Adjusted for land area troposphere CO₂ mass was higher for eastern and southern provinces than for western and northern provinces. Tropospheric CO₂ mass over China varied with season being highest in July and August and lowest in January and February. The average annual emission from provincial energy statistics of CO₂ by China was estimated as 10.3% of the average mass of CO₂ in the troposphere over China. The relationship between statistical emissions relative to tropospheric CO₂ mass was higher than 20% for developed coastal provinces of China, with Shanghai, Tianjin, and Beijing having exceptionally high percentages. The percentages were generally lower than 10% for western inland provinces. Provincial estimates of emissions of CO₂ were significantly positively related to provincial populations and gross regional products (GRP) when the values for the provincial municipalities Shanghai, Tianjin, and Beijing were excluded from the linear regressions. An increase in provincial GRP per person was related to a curvilinear increase in CO₂ emissions, this being particularly marked for Beijing, Tianjin, and especially Shanghai. The absence of detection of specific elevation of CO₂ mass in the troposphere above these municipalities may relate to the rapid mixing and dispersal of CO₂ emissions or the proportion of the depth of the troposphere sensed by GOSAT.     

共享社会经济路径下中国2020—2100年碳排放预测研究

碳排放和减碳经济代价研究日益受到学术界和决策者的关注，中国政府做出的关于争取在2060年前实现碳中和的表态引起了国际社会的热议。在此背景下，开展中国未来长时间序列碳排放的情景预测具有切实意义。基于可拓展的随机性环境影响评估模型（STIRPAT）评估了人口、经济和受教育程度对碳排放的影响，对比历史数据并验证了碳排放预测模型的准确性，结合共享社会经济路径（SSPs）情景的设定和模型参数，预测了5种情景下中国2020年至2100年的碳排放轨迹及经济代价。结果表明：（1）考虑碳排放达峰目标的实现，SSP3情景是中国未来发展的最佳情景，在此情景下，中国有望提前三年实现碳排放达峰目标；（2） SSP3情景可使中国年度总碳排放量和人均碳排放量处于相对其他四种情景的最低值，但需要付出累积GDP下降5.49%至8.80%的代价；（3）为完成在2060年前实现碳中和的承诺，中国政府在未来的40年需面对409.36-467.42 Gt的碳中和量；（4）2020年中国的碳排放强度将会较2005年水平下降40.52%至41.39%，2030年碳排放强度将会较2005年水平下降59.64%至60.75%。5种情境中，SSP5情景是降低碳排放强度的最佳情景，可最大程度地超额实现碳排放强度目标。未来，受经济发展、人口增长等重要因素影响，中国政府减碳压力将进一步加大。后疫情时代，考虑到能源供应的减少和高科技产业的发展，碳排放社会成本的上升将为中国创造一个使能源系统脱碳的机遇。中国应在"十四五"期间继续提升能源利用效率、升级产业结构、提倡低碳消费、实施隐含碳战略，以尽快实现碳减排目标。     

Panel estimation for the impacts of population-related factors on CO2 emissions: A regional analysis in China

A large accumulation of carbon dioxide emission have attracted much attention recently. The existing researches mainly focused on such impact factors of carbon dioxide emission as population, economy, technology and others. However, there is little specific guidance for the subdivision of demographic factors. This paper employed STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model to examine the impact of population size, per capita consumption, energy intensity, urbanization and aging population on CO₂ emissions by adopting panel data of 30 provinces from 1997 to 2012. Taking the climate change as a control variable, we can get the result that the population size, per capita consumption and energy intensity have strong explanatory power on CO₂ emissions in the three regions. The urbanization level has a positive influence on carbon emissions in the western region and has a negative effect in the central region, while it is not statistically significant in the eastern region. Aging population increases emissions in the eastern region, while decreases emissions in the central region and the western region.