| Global climate change has become a great challenge to the community with a shared future for mankind.Trade as an important means of resource allocation promotes the crossregional flow of resources,and also brings about the cross-regional allocation of traderelated embodied carbon emissions.The geographical separation of the initial input area,carbon emission area and final consumption area has made the original regional carbon emission pattern more complex.The traditional "consumption-based principle " and "income-based principle " approaches discuss the impact of final demand and initial input on embodied carbon emissions from different perspectives,which are incompatible with each other.The results can be "extreme" for a single-principle perspective of carbon emissions responsibility allocation based on different positions and economic assumptions,because the assumptions themselves are incompatible with each other.In real economic activities,the role of final demand and initial input in production activities is not separate but rather joint,and neither of them can explain the occurrence of production activities independently or be a single basis for the allocation of carbon emissions responsibility.Therefore,it is necessary to analyze the provincial industrial embodied carbon emissions in China from an integrated framework perspective,and to explore the impact of the interactive push and pull effects of final demand and initial inputs on production activities.In this research,an optimized model for embodied carbon analysis from an integrated perspective is proposed.Accordingly,the characteristics of China’s provincial industries’ embodied carbon in 2012,2015 and 2017 are discussed from the perspective of the mutual push-pull effects of final demand and initial inputs,respectively.By comparing the characteristics of the spatio-temporal distribution of production-based principle carbon emissions(PP),consumption-based principle carbon emissions(CP),income-based principle carbon emissions(IP),the relative scale level of IP pulled by final demand(CPI),and the relative scale level of CP pushed by initial inputs in the optimized model(IPC＿new)in each province and industry of China,it is found that PP,CP and IP have some similarity in the distribution pattern among provinces and industries.The spatio-temporal variation of carbon emissions in China is large,with strong clustering characteristics,and the key nodes of high carbon emissions are always concentrated in a few key provinces and key industries under different principle perspectives.During the study period,the national CPI declined slightly,and the pull effect of CPI in 2012,2015 and 2017 were: 10975.53 Mt C,9725.85 Mt C and 10844.88 Mt C,respectively;in comparison,the push effect of IPC＿new in 2012,2015 and 2017 were.3700.05 Mt C,4099.15 Mt C and 3344.95 Mt C,with a higher drop than CPI.In terms of scale volume,China’s production activities,and the carbon emissions associated with them,are still driven to a greater extent by the dramatically growing final demand,and the pull of final demand on IP is extremely significant,with the interaction forces of initial input and final consumption activities There are obvious imbalances in the interaction between initial input and final consumption activities,and significant industrial heterogeneity and provincial heterogeneity.In terms of time-varying trends,both CPI and IPC＿new in China decrease to varying degrees,but IPC＿new decreases significantly more than CPI.In the coming period,as China’s consumption market continues to develop and grow,final demand will likely continue to maintain a strong pull effect on China’s carbon emissions.Moreover,this study compares the spatio-temporal patterns of embodied carbon transfer and its evolutionary characteristics at the provincial and industrial level in China from a single-principle perspective and an integrated perspective using complex network analysis in two dimensions: overall network properties and key network nodes,respectively.The results show that the network correlations of CP,IP,CPI and IPC＿new in each industry of Chinese provinces have increased and have a high modularity level during 2012-2017.Individual nodes in these networks have a strong carbon emissions network diffusion effect.In terms of weighted average degree,the average pulling effect of CPI is higher than the average pushing effect of IPC＿new.In terms of flow characteristics,the CP transfer network shows the characteristics of concentrated outflow and decentralized inflow,while the IP transfer network shows the characteristics of concentrated inflow and decentralized inflow.In comparison,the CPI and IPC＿new networks flow more evenly between provinces and industries,where the key nodes of higher weighted in degree of CPI and IPC＿new transfer networks are dominated by the construction industry and the electricity and heat supply industry.During the study period,nodes with higher betweenness centrality are constantly changing and relatively fragmented at the industry level.S14 in Hebei province is the only hub node that is in the top 20 CP betweenness centrality and IP betweenness centrality in all three years.The betweenness centrality of nodes in both the CPI and IPC＿new networks are calculated to be 0.This may be due to the fact that the betweenness centrality is calculated based on the premise of an undirected and unweighted network.When the influence of weights in the network is not considered,the Leontief and Ghosh models,which are applied to both the CPI and IPC＿new models,will cancel each other out and end up with a zero betweenness centrality.This study concludes that the results of the indicator analysis of the betweenness centrality also illustrate the rationality of the optimized model,because it accounts for the cumulating influence of the Leontief model and the Ghosh model in both directions by considering the carbon emission weights of each node.This study applies structural decomposition analysis(SDA)to discuss the factors influencing the temporal variation of embodied carbon in Chinese provincial industries from the integrated perspective in terms of carbon emission technology effect(CTE),production structure effect(PSE)and final demand scale effect(FDSE)or initial input scale effect(IISE).According,the final demand scale effect and initial input scale effect are refined into different final demand types and initial input types,respectively.The results show that during 2012-2017,the national CPI scale volume decreases from 10975.53 Mt C to 10844.88 Mt C,or 1.2%,of which,the CTE contributes-21.9%,the PSE contributes-14.20%,and the FDSE contributes 33.8%;the IPC＿new scale volume decreases from3700.05 Mt C to 3344.95 Mt C,a decrease of 9.60%,in which the CTE,PSE,and IISE contribute-30.24%,-17.13%,and 38.86%,respectively.In terms of specific types of FDSE,gross fixed capital formation is the largest positive contributor.China’s final demand growth is mainly led by investment,not by consumption.The internal impact of the export size effect is the only type of final demand that makes a negative contribution during the study period.As to IISE,the scale effect of labor compensation is the largest positive contributor.The degree of scale effect of fixed asset depreciation is higher than that of operating surplus,and it is necessary to strengthen the ability of enterprises to make profits,which in turn empowers them to develop and promote low-carbon technologies.There is heterogeneity in the interaction between final demand and initial inputs and the characteristics of their main influencing factors at the provincial and industry levels in China,and specific problem-specific analysis is needed when formulating precise carbon reduction strategies that are differentiated by province and industry.Last but not least,this paper proposes a new scheme for the allocation of shared carbon emissions responsibility(SP),in which the responsibility of producers(as well as income earners)is represented by the relative pushing effect of initial inputs,and the responsibility of consumers is represented by the relative pulling force of final demand,using the degree of mutual influence between final demand and initial input as the basis for determining the SP allocation coefficient.Accordingly,a multi-responsibility body’s SP allocation scheme is proposed.In 2017,for example,the top five provinces with the largest increase in carbon emission responsibility under SP compared with the unallocated PP are Guangdong Province,Zhejiang Province,Beijing City,Chongqing Province,and Jiangsu Province,with an increase of 304.36 Mt C,250.09 Mt C,and 154.09 Mt C,respectively.The top five provinces with the largest decreases in carbon emission responsibility are Inner Mongolia Autonomous Region,Hebei Province,Shandong Province,Shanxi Province,and Xinjiang Uygur Autonomous Region,with decreases of-224.59 Mt C,-213.55 Mt C,-154.83 Mt C,-125.39 Mt C,-104.69 Mt C,and-104.69 Mt C,respectively.Mt C,-104.69 Mt C.This study proposes an optimized an integrated embodied carbon emissions analysis model,which can provide an important complement to the traditional single-principle analysis,and then portray the embodied carbon emissions characteristics of Chinese provinces and industries in a more systematic way.Meanwhile,to address the problem that it is difficult to determine the coefficients for the allocation of carbon emission responsibility under the existing "shared responsibility" perspective,this study adopts the relative magnitude of the interaction between final demand and initial input on embodied carbon emissions calculated by the optimization model as the basis for the allocation of coefficients for the shared responsibility of carbon emission,and proposed a new scheme for the allocation of embodied carbon responsibility which involves multiple responsible parties.Combining the results of the analysis,this study proposes specific policy recommendations from three perspectives: "who should reduce","how much to reduce" and "how to reduce".In order to achieve China’s carbon peaking and carbon neutrality goals,it is necessary to formulate precise and scientific carbon reduction plans from the perspective of the correlation between "production" and "consumption" for each province and industry. |
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