Multiregional Input-Output Analysis of Spatial-Temporal Evolution Driving Force for Carbon Emissions Embodied in Interprovincial Trade and Optimization Policies: Case Study of Northeast Industrial District in China

Abstract

In the counties with rapid economy and carbon emissions (CEs) growth, CEs embodied in interprovincial trade (CEs-PT) significantly impacts the CEs amount and structure and represents a key issue to consider in CEs reduction policies formulation. This study applied EEBT and two-stage SDA model to analyze the characteristics and driving force of spatial-temporal evolution for net CEs-PT outflow in the Northeast Industrial District of China (NID). We found that, during 1997-2007, the net CEs-PT flowed out from NID to 16 south and east provinces, then to 23 provinces all over China, and its amount has increased 216.798Mt (by 211.67% per year). The main driving forces are technology and demand (further decomposed into structure and scale matrix); the contribution are 71.6418 Mt and 145.1562 Mt. Then, we constructed coupling relationship model and took the top three industries with the greatest net CEs-PT outflow (farming, forestry, animal husbandry and fisheries, electricity and heat production and supply, petroleum processing, coking, and nuclear fuel processing) as examples, adjusted the interprovincial trade constructions, scales, and objects, to reduce the CEs-PT with lower costs, greater effect, and more equitability. The achievement could provide reference for formulating CEs reduction policies for similar areas in the world characterized by rapid growth of economy and CEs.

Drivers of virtual water flows on regional water scarcity in China

“…Thus, MRIO is widely used in the calculation of virtual water flows (Guan et al, 2014;Lenzen et al, 2013;Qu et al, 2017;White et al, 2015;Zhao et al, 2015). The structural decomposition analysis (SDA) has often been combined with the MRIO to analyze the drivers of emissions and resources consumption, respectively (Cheng et al, 2017;Mi et al, 2017b).…”

Energy Flows and Carbon Footprint in the Forestry-Pulp and Paper Industry

“…The proportion of each tributary depends on the actual situation of production activities. Based on the supply chain network structure and the input-output relationship matrix of each level, it can be traced back to the carbon resources of the upstream relevant sectors from the downstream product demands [49].…”

Dynamic Input–Output Analysis of a Carbon Emission System at the Aggregated and Disaggregated Levels: A Case Study in the Northeast Industrial District

“…As a result, in most urban regions, particularly those with greater carbon emissions, the formulation of emission reduction policies lacked a comprehensive assessment at the aggregated and disaggregated sector-level, and the reduction effect was much weaker [16]. These policies ostensibly reduced the carbon emissions of a region or an industry, while actually increasing the emissions in another region or industry through trade associations [17]. Thus, these policies reduced the effects of emission reduction or even generated a negative effect, wasted the investment in emissions reduction, and created an unfair distribution of reduction responsibilities.…”