Carbon Emissions In Provinces Research Articles

Spatio-temporal Heterogeneity of Factors Influencing Transportation Carbon Emissions in Provinces Along the Belt and Road

The administrative units of 17 provinces （autonomous regions and municipalities directly under the Central Government） along the "Belt and Road" were selected as basic spatial units to calculate the provincial traffic carbon emissions along the "Belt and Road" from 2000 to 2021. On the basis of analyzing the spatial and temporal characteristics of traffic carbon emissions by using the spatial autocorrelation method, the spatial and temporal heterogeneity of influencing factors of traffic carbon emissions was explored by combining a fixed-effect regression model and geographic detector. The results show that： ① The provincial traffic carbon emissions along the "Belt and Road" had significant spatial positive correlation, and the overall trend was upward. Additionally, the cluster evolution of high and low values of traffic carbon emissions presented the characteristics of polarization in space. The high value cluster area was mainly distributed in the open leading area, and the low value cluster area was mainly distributed in the core area of the silk road. ② Opening-up level and vehicle ownership were the positive driving factors of carbon emissions from transportation, whereas energy intensity, transportation structure, industry development scale, and government intervention were the negative driving factors. ③ Energy intensity and transportation structure were the main driving factors for the spatial variation of transportation carbon emissions, and most of them would produce nonlinear enhancement when they were spatially superimposed with other factors, that is, there was strong synergy among driving factors. The results showed that the provincial traffic carbon emissions along the "Belt and Road" were affected by the surrounding areas, the influence degree was increasing, and there was synergy between the key driving factors of traffic carbon emissions. Therefore, it is suggested that the provinces along the "Belt and Road" should fully consider the spatial and temporal heterogeneity of traffic carbon emission influencing factors and formulate differentiated traffic carbon emission reduction policies.

Analysis of spatiotemporal evolution characteristics and peak forecast of provincial carbon emissions under the dual carbon goal: Considering nine provinces in the Yellow River basin of China as an example

At present, China is facing substantial pressure to reduce carbon emissions (CE). In the context of China's dual carbon goals, the accounting and peak prediction of CE in provinces along the Yellow River Basin (YRB) are crucial to China's carbon reduction strategy. In this study, the IPCC inventory method was used to calculate the CE of the provinces in the YRB, and the spatial and temporal evolution characteristics of CE are described. The Tapio decoupling model was used to explore the decoupling relationship between CE and economic growth. By establishing the stochastic impacts by regression on population, affluence and technology (STIRPAT)-ridge model, based on the impacts of CE drivers, the carbon peak of each province is predicted, and a differentiated carbon peak path is proposed. The results indicated that from 2005 to 2020, the total CE amount in the YRB continued to rise, but the growth rate continued to decline. Provinces experienced many changes involving strong decoupling, and the decoupling state varied. Factors such as the permanent resident population, economic level, technological level, urbanization level, industrial structure, and energy intensity drove CE changes, of which population was the main CE driving force. CE in the YRB will peak between 2030 and 2044, and the cumulative emissions will be in the range of 6913.96–16248.6 Mt CO2eq, while 2030 is suggested as the best year to peak. The research results provide a scientific basis for different regions to formulate differentiated carbon emission reduction plans.

Changes in Land Use Carbon Emissions and Coordinated Zoning in China

Carbon emissions from land use changes have become one of the main sources of regional carbon emissions. In order to explore its changes, based on the MCD12Q-LUCC data of MODIS from 2001 to 2019 using the carbon emission coefficient method, clustering, and outlier analysis method, the spatial characteristics of land use carbon emissions in various provinces in China in the past 19 years were discussed from the perspectives of carbon emission economy contributive coefficient, carbon ecological support coefficient, and their coupling and coordination relationship. The results showed that:① from 2000 to 2019, the national land use carbon emissions increased significantly; however, after 2011, the growth rate of carbon emissions became flat, whereas the growth of carbon sinks was relatively slow, and the gap between the two was still large. ② Clustering and outlier analysis showed that during the study period, the high-value agglomeration centers of land use carbon emissions in various provinces and cities across the country shifted from Guangdong, Jiangsu, and other provinces to Hebei, Shanxi, Inner Mongolia, and other provinces, and the agglomeration status became increasingly obvious. ③ The economy contributive coefficient of carbon emissions in all provinces and cities across the country had the spatial characteristics of being high in the south and low in the north, and the ecological support coefficient gradually developed from high in the west to low in the east, followed by that in the north, and the coupling coordination between the two showed a downward trend. ④ Based on the economy contributive of carbon emissions and carbon ecological support, this study divided the provinces into four categories:low-carbon maintenance area, economic development area, carbon sink development area, and comprehensive optimization area. We also put forward our own development suggestions, striving to achieve carbon neutrality and low-carbon sustainable development.

Analysis of provincial CO2 emission peaking in China: Insights from production and consumption

China has pledged to peak its carbon dioxide emissions by 2030, and the provincial governments have a common but differentiated responsibility for cutting emissions, depending on their conditions. Requiring all provinces to peak CO2 production-based emissions by 2030 may harm some regional economies because of the different industrial divisions in each Chinese province. In addition to production-based accounting (PBA), the study on the peaking of carbon emissions on consumption-based accounting (CBA) is a beneficial supplement to the research of PBA to comprehensively understand the basic situation and possible future paths of carbon emissions in all provinces in China. This research uses scenario analysis and the Monte Carlo simulation to simulate the CO2 path of Chinese provinces on production- and consumption-based accounting, which is based on China’s multi-regional input–output (MRIO) table in 2017 from the Chinese Emissions Accounts and DataSets (CEADs). This study finds that the provincial peaking time of CBA is 4–5 years later than that of PBA on average. Carbon transfer is the source of the difference in carbon emissions between PBA and CBA in different regions. We suggest that the government should pay attention to PBA and CBA when formulating policies. The region where PBA CO2 emissions reach peaking first should pay attention to the situation of CBA CO2 emissions, and the region where CBA CO2 emissions peak first should pay attention to the decline of PBA CO2 intensity. Finally, the government should encourage some developed net carbon import regions to provide transfer assistance of capital, technology, and talents to their carbon import regions to promote the scientific, fair, and efficient peaking of carbon emissions in provinces and the whole country.

Responses of carbon emissions to corruption across Chinese provinces

In response to the recent growth of multitudes of theoretical literature analysing the corruption impact on the economy and environment, this paper subjects the corruption–carbon emission relationship in China to a detailed empirical examination through the autoregressive distributed lag modelling approach and panel quantile regressions. Based on panel data from Chinese provinces, spanning the period 1998–2016, this study explores the impact of long- and short-term corruption on per capita carbon emissions by considering the heterogeneous distribution of those emissions. The results document that corruption increases per capita carbon emissions in Chinese provinces in the short run, reducing per capita carbon emissions in the long run. Moreover, an increase in corruption leads to an increase in carbon emissions per capita in all quantiles, indicating that these emissions increase with corruption severity. The coefficients in low quantiles are slightly larger than those in high quantiles, indicating that corruption leads to more carbon emissions in provinces with lower per capita carbon emissions.

Comparative analysis of regional carbon emissions accounting methods in China: Production-based versus consumption-based principles

Abstract The analysis of the characteristics of carbon flows between regions is critical for China to deploy effective regional mitigation strategies. Different accounting principles exert a significant influence on China’s regional carbon emissions, especially the fairness of carbon emission reduction targets, and the responsibility for reducing emissions. An input-output model was established, based on the input-output data of 2007 and 2012, to evaluate the characteristics of regional carbon flow and the change in carbon emission from different industries, and to analyse the changes in carbon emissions under production-based, and consumption-based, principles in 30 provinces. The results show that the carbon emissions in each region and sector are different: from the eastern region to the western region they show a downward trend, and the embodied carbon flow accounts for a large proportion of the total. Secondary industries are a major contributor to carbon emissions and there is a signifiant influence exerted on the results of carbon emission accounting in various provinces of China under different accounting principles. Therefore, to meet national emission reduction targets, the provinces need to pay considerable attention to the choice of accounting principles and achieve the principle of fairness and impartiality. It will provide a reference for the future division of responsibility for carbon emissions in provinces of China.

Quantification and driving force analysis of provincial-level carbon emissions in China

Accurately understanding the real situation of China’s carbon emissions at the provincial level is not only crucial for realizing carbon mitigation in China, but also of great importance for tackling climate change on a global scale. This paper uses detailed energy consumption data subdivided within each fuel type and sector-specific emission factors, to calculate both direct and indirect energy-related CO2 emissions for China’s provinces, and offers provincially-targeted policy proposals on emission reductions. The results uncover large divergences in calculation of provincial CO2 emissions between production-based and consumption-based accounting approaches. Considering the large amount of interprovincial CO2 emission transfers embodied in secondary energy deployment, the consumption-based accounting performs better in reflecting the actual features and responsibilities of carbon emissions in the provinces of China. From the consumption perspective, Shandong, Hebei, Jiangsu, Inner Mongolia, Guangdong, Henan, Liaoning and Shanxi contributed to the majority of China’s carbon emission growth during the period of 1996–2013, while Xinjiang, Sichuan, Anhui, Tianjin and Hunan were playing an increasingly important role. These two groups of provinces should be regarded as the key regions for national emissions abatement. Meanwhile, the energy industry was the top sectoral contributor to CO2 emissions growth in most provinces, while service and household sectors made increasingly significant impacts on driving up provincial emissions. Together with ongoing efforts in the energy sector, the mitigation of CO2 emissions in service and household sectors deserves much more attention, especially in the transportation and urban household sectors. Furthermore, the temporal trends of carbon emissions in various provinces, as well as their factor and sectoral driving forces, varied greatly with each other. Therefore, each of the provinces should formulate emission reduction policies according to their own situation, and target the key driving factors and sectors of carbon emissions at the current stage or in the near future.

Regional Disaggregation of Energy Consumption Target: The Case of Henan Province

An approach to determine energy consumption control target allocation based on analytic hierarchy process, Ward's clustering algorithm, and differential adjustment method is proposed in this study. A case study of the allocation of control targets in Henan by 2020 is then conducted via the proposed method. The results show that: The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Regions with high cardinality of energy consumption have to shoulder the largest reduction, whereas regions with low energy intensity and poor economic conditions met the minimum requirements for energy consumption in 2020.

Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition

An approach to determine carbon emission reduction target allocation based on the particle swarm optimization (PSO) algorithm, fuzzy c-means (FCM) clustering algorithm, and Shapley decomposition (PSO–FCM–Shapley) is proposed in this study. The method decomposes total carbon emissions into an interaction result of four components (i.e., emissions from primary, secondary, and tertiary industries, and from residential areas) which composed totally by 13 macro influential factors according to the KAYA identity. Then, 30 provinces in China are clustered into four classes according to the influential factors via the PSO–FCM clustering method. The key factors that determine emission growth in the provinces representing each cluster are investigated by applying Shapley value decomposition. Finally, based on guaranteed survival emissions, the reduction burden is allocated by controlling the key factors that decelerate CO2 emission growth rate according to the present economic development level, energy endowments, living standards, and the emission intensity of each province. A case study of the allocation of CO2 intensity reduction targets in China by 2020 is then conducted via the proposed method. The per capita added value of the secondary industry is the primary factor for the increasing carbon emissions in provinces. Therefore, China should limit the growth rate of its secondary industry to mitigate emission growth. Provinces with high cardinality of emissions have to shoulder the largest reduction, whereas provinces with low emission intensity met the minimum requirements for emission in 2010. Fifteen provinces are expected to exceed the national average decrease rates from 2011 to 2020.