Multiregional Input-Output Analysis of Spatial-Temporal Evolution

Dec 5, 2017 - (29) Thus, these policies reduced the CEs reduction effect or even generated a negative effect, wasted CEs reduction investment and crea...
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Multi-regional Input-output Analysis of Spatial-temporal Evolution Driving Force for Carbon Emissions Embodied in Interprovincial Trade and Optimization Policies: A Case Study of Northeast Industrial District in China Hao Cheng, Suocheng Dong, Fujia Li, Yang Yang, Shantong Li, and Yu Li Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04608 • Publication Date (Web): 05 Dec 2017 Downloaded from http://pubs.acs.org on December 10, 2017

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Environmental Science & Technology

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Multi-regional Input-output Analysis of Spatial-temporal

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Evolution Driving Force for Carbon Emissions Embodied in

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Interprovincial Trade and Optimization Policies: A Case Study

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of Northeast Industrial District in China

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Hao Cheng,a Suocheng Dong,a Fujia Li,a,* Yang Yang,a,b Shantong Li,c and Yu Lia

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(a. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; b. University of Chinese Academy of

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Sciences, Beijing 100049, China; c. Department of Development Strategy and Regional Economy, Development Research Center, State

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Council, Beijing 100010, China)

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ABSTRACT: In the counties with rapid economy and carbon emissions(CEs) growth, CEs embodied in

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interprovincial trade(CEs-PT) significantly impacts the CEs amount and structure, and represents a key issue to

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consider in CEs reduction policies formulation. This study applied EEBT and two-stage SDA model to analyze the

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characteristics and driving force of spatial-temporal evolution for net CEs-PT outflow in the Northeast Industrial

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District of China(NID). We found that, during 1997-2007, the net CEs-PT flowed out from NID to 16 south and

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east provinces, then to 23 provinces all over China, and its amount has increased 216.798Mt(by 211.67% per year).

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The main driving forces are technology and demand(further decomposed into structure and scale matrix), the

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contribution are 71.6418Mt and 145.1562Mt. Then, we constructed coupling relationship model and took the top

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three industries with the greatest net CEs-PT outflow(Farming, forestry, animal husbandry and fisheries,

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Electricity and heat production and supply, and Petroleum processing, coking and nuclear fuel processing) as

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examples, adjusted the interprovincial trade constructions, scales and objects, to reduce the CEs-PT with lower

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costs, greater effect and more equitable. The achievement could provide reference for formulating CEs reduction

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policies for similar areas in the world characterized by rapid growth of economy and CEs.

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TOC Art

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1. INTRODUCTION

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Carbon emissions embodied in interregional trade (CEs-RT) has a significant impact on the

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amount and structure of regional carbon emissions (CEs), and it is an important factor that should

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be considered in any regional CEs reduction policies formulation and adjustment. Currently, this

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issue has attracted much attention in the literature,

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CEs-RT have proliferated.

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international trade (CEs-NT), while the CEs embodied in interprovincial trade (CEs-PT) within a

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single country has received not enough attention.

1

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3,4

5,6

and studies focusing on the transference of

However, most studies have analyzed the CEs embodied in

7-10

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In fact, for countries with larger economies and greater CEs, they usually have more

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provinces and more active interprovincial trade; and the CEs-PT has a greater influence on the

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CEs in an administrative region than the CEs-NT,

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than between countries. To engage with this issue, some scholars have conducted beneficial

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explorations of the CEs-PT in different countries. McGregor et al. analyzed the CO2 pollution

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content of interregional trade flowed between Scotland and the rest of the UK and found that the

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interregional environmental spillovers within the UK was significant and that a CO2 ‘trade balance’

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existed between Scotland and the rest of the UK.

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model to track interregional carbon flowed in the Jing−Jin−Ji Area by combining network analysis

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and input-output analysis, and found that the CO2 emissions embodied in products was only partially

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controlled by a region and that the controlled carbon accounted for approximately 70% of the total

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embodied carbon.

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relationship inter-regional spillover of CO2 emissions and domestic supply chains for 2002 and

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2007 within eight regions of China.

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obtain a regional map of carbon footprints within eight regions of China from 1997 to 2007.

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Zhang et al. applied EEBT model to clarified the provincial and sectoral contributions to national

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emissions. Zhang et al. used provincial-level MRIO model to study the trends and disparities of

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consumption-based emissions from Chinese provinces during the period of 2002-2007. Liu et al.

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provided a dynamic analysis of carbon emissions embodied in consumption and export demand-

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as trade barriers are lower between provinces

Chen and Chen presented a hybrid network

Meng et al. applied the inter-regional input-output model to explain the

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Tian et al. applied carbon footprints and SDA analysis to 15

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supply chains at the sub-national level based on the MRIO tables for 1997 and 2007. Zhang and

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Tang used MRIO model and logarithm mean Divisia index approach to analyze the changes in

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China's carbon embodied in exports at the national and provincial levels. It is noteworthy that Su

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and Ang have conducted a continuing study of the CEs-RT, at the level of spatial aggregation and

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sector aggregation, from the national scale and the global scale.

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to be meaningful for revealing CEs-PT transference.

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19-24

These studies are considered

The transfer quantity and direction have attracted the bulk of the attentions in the CEs-PT 25-28

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researches

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transference, while little research has implemented optimization policies based on the driving

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force mechanisms analysis. What’s more, for the lack of such studies, the researches on

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formulating policies to reduce CEs effect have been limited. Both the depth and the amount of the

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studies are far from meeting the actual needs of CEs reduction. As a result, in most countries,

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particularly those with greater CEs, the formulation of CEs reduction policies lacked provincial

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coordination and cooperation, and the reduction effect was much weaker for the interference and

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restriction of the CEs-PT. These policies reduced the CEs of a region or an industry on the surface,

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while actually increased the CEs in another region or industry through trade associations.

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these policies reduced the CEs reduction effect or even generated a negative effect, wasted CEs

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reduction investment and created an unfair distribution of reduction responsibility.

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gap in the literature, we urgently need to analyze the spatial-temporal evolution of the CEs-PT

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transference over a long period, explore the sources and driving force mechanisms, and implement

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optimization policies to coordinate the relationship between interprovincial trade and the CEs-PT.

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Ultimately, the result could guide policymaker to formulate CEs reduction policies with lower

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costs, better reduction effects, more equitable and greater cooperation between regions.

, but these researches have less consideration for the driving force of the CEs-PT

30,31

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Thus,

To fill this

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The Northeast Industrial District of China was selected as a case study for the CEs-PT

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research from 1997 to 2007, because in this area and during this period, the CEs had a greater

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contribution rate of China, and GDP and CEs had grown rapidly. Based on the 1997, 2002, and

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2007 input-output (IO) tables of 30 provinces in China, this study applied emissions embodied in

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bilateral trade model(EEBT), to analyze the characteristics of spatial-temporal evolution for the

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CEs-PT transference between the study area and other provinces. Then we applied two-stage

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structural decomposition analysis(SDA) approach to analyze the driving force for CEs-PT

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transference, including technology driving force and demand driving force (which was

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decomposed into structure driving force and scale driving force). Focused on these driving forces,

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we built a coupling relationship model(CR) of net commodity value outflow change and net CEs-

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PT outflow change to adjusted the regional industrial structure and the interprovincial trade

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structure in order to scientifically and efficiently reduce the CEs-PT, achieve the CEs reduction

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target with lower costs, better effects and more equitable. This study will provide a reference for

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formulating CEs reduction policies with lower costs, better effects and more equitable in China

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and in similar areas worldwide characterized by rapid growth in both the economy and the CEs.

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2. MATERIALS AND METHODS

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2.1. Case Study. The study area is the Northeast Industrial District of China (NID), including

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three traditional industrial provinces of Liaoning Province, Jilin Province and Heilongjiang

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Province in the northeast of China. The total area of the NID is 787.3 thousand square kilometers,

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accounting for 8.2% of China. From 1997 to 2007, the GDP of the NID increased from 776.684

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billion Yuan to 2,355.299 billion Yuan, with an average annual growth rate of 20.32%, accounting

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for 8.86% of China’s GDP in 2007 (Figure 1). The NID had long maintained close interprovincial

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trade relationships with other provinces in China and had made outstanding contributions to

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China’s economy. The NID was once an important engine for the rapid economic growth in China.

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Meanwhile, the NID had also greatly contributed to the rapid CEs growth in China. From

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1997 to 2007, the total CEs of the NID increased from 103.0425 Mt to 364.0169 Mt, with an

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average annual growth rate of 25.33%, accounting for 20.20% of China’s CEs in 2007 (Figure 1),

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which far exceeded the proportions of the area and GDP. Thus, clearly the industrial structure

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tended toward heavy industry, and the CEs reduction pressure was huge. Currently, the NID is

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facing the double pressures of the economic downturn, as China's economic growth has slowed,

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and the CEs reduction. It is thus urgent to formulate scientific and reasonable CEs reduction

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policies to improve the reduction effect with the reduction cost in minimize.

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Figure 1. GDP and CEs of the Northeast Industrial District from 1997 to 2007

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Therefore, considering the characteristics of heavy industrial structure, high CEs and high

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interregional trade volume, the NID is very typical and representative of those regions with rapid

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growth of economy and CEs in the world. The NID was selected as a case study for assessing the

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dynamic impact of the CEs-PT on CEs, implementing optimization policies to reduce CEs and

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providing a reference for similar regions worldwide.

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2.2. Data Sources. In this study, we used the 1997 IO tables (with 40 sectors for 30 provinces, 32

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excluding Tibet)

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Tibet)

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Council, P.R.C.. The IO table is compiled once every five years, and the latest data was published

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in 2007. To unify the data, we merged industries into 28 sectors.

33,34

and the 2002 and 2007 IO tables (with 42 sectors for 30 provinces, excluding

for China, which are compiled by the Development Research Center of the State

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Owing to the lack of official CO2 emissions data at the provincial level, we estimated the

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coefficients of CO2 emissions and energy consumption for 30 provincial regions based on the

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IPCC reference approach.

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different energy types used in different sectors of different regions into standard coal, in order to

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achieve internal integration of energy type differences.

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In the process of collecting energy consumption data, we converted

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Firstly, we collected energy consumption data from the table named “Energy Consumption

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by Sector” from 30 Provincial Statistical Yearbooks in 1997, 2002 and 2007. If there is no this

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table, we replaced it with the table named “Overall Energy Balance Sheet (OEBS)” from

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Provincial Statistical Yearbooks or the table named “Energy Balance of Region(EBR)” from

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China Energy Statistical Yearbooks. The ECS could provide the energy consumption date of every

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sector directly. While the OEBS or EBR could provide the energy consumption data of several

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sectors or the total energy consumption data of some sectors. For the data given directly, it can be

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used directly. For the total data, we decomposed it by the proportion obtained from the table

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named “Consumption of Energy by Sector and Major Variety” or “Main Energy Consumption of

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Industrial Enterprises above Designated Size by Sector” from Provincial Statistical Yearbooks.

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Then we converted the actual amount of energy consumption into the standard coal, in

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accordance with the energy conversion coefficient provided by the Intergovernmental Panel on

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climate change (IPCC) and Energy Statistics Knowledge Manual (ESKM).

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Finally, we converted the energy consumption data into carbon emissions data, in accordance with the carbon emissions coefficient provided by IPCC.

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2.3. Methodology.

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2.3.1 Multi-regional input-output analysis modeling for spatial-temporal evolution—EEBT.

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There are two common approaches can be applied to measure embodied emissions: one

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considers total bilateral trade between regions (EEBT approach) and the other considers trade to

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final consumption and endogenously determines trade to intermediate consumption (MRIO

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approach).38,39 They are both constructed based on multi-regional input-output tables(MRIO

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tables), but they have different advantages. The EEBT model is relevant for considering the

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environmental impacts of aggregated exports from and imports to a region. It has the

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transparency property and is considered superior when analyzing bilateral trade and climate

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policy. The MRIO model has the advantage of reflecting interregional spillover and feedback

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effects. It is more applicable to the analysis of final consumption and analogous to LCA which

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consider the total emissions from raw-material extraction to final consumption.39-41 In this

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research, we want to formulate the optimization policies based on the interregional bilateral trade

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and CEs-PT transference. It is too complex and unnecessary for considering the interregional

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spillover and feedback effects. Comparing the above different advantages of two models, we

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chose EEBT model to analyze spatial-temporal evolution of CEs-PT, for it is more suitable for

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our research.

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MRIO tables and their applications have generated substantial interest at the forefront of

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environmental policy debate.42,43 To perform a multi-regional input-output study requires a

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considerable amount of data, and much of these are not directly available. Therefore, various

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approximations and simplifications should be used in the process of multi-regional input-output

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compilation. Generally, a gravity model would be used to estimate an interregional commodity

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flow matrix based on regional IO tables, and then the well-known iterative procedure of bi-

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proportional adjustment of the RAS technique would be used to find a balanced multi-regional

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input-output table.44,45 We obtained MRIO tables by analyzing interprovincial trade using the

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cross-entropy method and a gravity model. Table 1 demonstrates the structure of the MRIO table

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in region r. The region r is one of m regions in a country, and it has n industry sectors. In our

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research, one province has one MRIO table in one year. So, there are total 90 MRIO tables for 30

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provinces in China in 1997, 2002 and 2007. Based on these tables, we applied the EEBT model to

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analyze the CEs-PT transference.

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Table 1. Multi-regional input-output table of region r in a country with m regions(R) and n sectors(S)

Region r

Industry

Intermediate use

sector

Final

Total

Value flow out from Region r in

use

output

interprovincial trade

S1 ... Sj ... Sn Intermediate input

S1

. .. Si

. .. Sn



 …  …  . ..

. ..



...

 …  …  ...

. ..



. ..

 …  … 





. ..

. ..





. ..

. ..







Added value

 …  … 

Total input

 …  … 

Value flow into Region R1

   …  … 

R1

...

Rd

...

Rm

 



 



 

...

...

 



 



...

...

 



 



...

 

...

 



r in interprovincial

. .. Rd

trade

. .. Rm



...

. ..



...

   …  … 

...

. ..



...

 …    … 

173 

174

where  refers to intermediate input from sector i to sector j in region r (i,j=1……n, n=28 in

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our study),  is the final use of sector i,  is the total output of sector i,  is the added value of



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 sector j, and  is the total input of sector j. 

represents the commodity value transference of

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sector i from region r to region d, and  represents the value transference of sector j from region

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d to region r (d,r=1……m, m=30 in our study).



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Direct carbon emissions (DCEs) accounting, which is used under the Kyoto Protocol, is a

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straightforward approach to accounting for CEs.44 In this study, we represented the DCEs of the

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country as C, and the DCEs of region r as  . The DCEs of region r, when divided by the total

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output in region r, could indicate the quantity of DCEs per unit of output, namely, the direct

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carbon emissions factor of region r, expresses as  . It is calculated by the following formula:

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 =  /

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From the horizontal angles in Table 1, we obtained

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 =  −  

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where X is the total output matrix, Y is the final use matrix for all regions, and A expresses

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the local inter-industry requirements (the local technical coefficient matrix). The matrix I − 

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is generally called the Leontief inverse matrix or the cumulative coefficient matrix.

(1)

(2)

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We used Ex instead of Y and obtained the following:

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 =  −  !"

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where Ex expresses the value outflow in interregional trade, and Xe is the total input for Ex.

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By multiplying both sides of Formula (3) by F, the CEs-PT outflow can be calculated as

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(3)

follows:

 = # ×  = # × % −  !" & = ' × × % −  !" &

(4)

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where ( expresses the CEs-PT outflow, ' is the CEs coefficient, which expresses the

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conversion from standard coal to carbon emission, and e is the energy consumption coefficient,

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which expresses the conversion from different type energy consumption to standard coal. The e is

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different for different fuel types.

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The NID includes Liaoning Province, Jilin Province and Heilongjiang Province. These three

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provinces have the similar resource endowments, economic levels, industrial structures and levels

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of CEs in the whole trade chain. This study combined the three provinces into a composite to

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analyze the CEs-PT transference from the NID to other provinces.

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The CEs-PT outflow from the NID to 27 other provinces can be demonstrated by the following formula: +,

+,

+,

+,

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)* = -* + /* + 0*

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where 1 is the CEs-PT outflow from sector i of the NID to sector j of the 27 other

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provinces (d=1……27, i,j=1……28), 2 expresses Liaoning Province, 3 expresses Jilin

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Province, and 4 expresses Heilongjiang Province.

(5)









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According to the data and the methodology described above, we could obtain the net CEs-PT

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outflow and the net commodity value outflow in interprovincial trade of 28 sectors between NID

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with other 27. Then net CEs-PT outflow can be calculated as follows: +,

+,

,+

213

5)* = )* − *)

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where 67 expresses the net CEs-PT outflow from the NID to 27 other provinces, 7

215

(6)







expresses the CEs-PT outflow from the NID, and  7 expresses the CEs-PT inflow to the NID.

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On the same principle, the net commodity value outflow could be calculated as follows:

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5!)* = !)* − !*)

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where 687 expresses the net commodity value outflow from the NID to 27 other provinces,

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87 expresses the commodity value outflow form the NID, and 8 7 expresses the commodity

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value inflow to the NID.

+,

+,

,+

(7)







221

Based on the above EEBT model, we could analyze the characteristics of spatial-temporal

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evolution for the net CEs-PT outflow between the NID and the other provinces from 1997 to 2007.

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2.3.2 Modeling for driving force analysis—Two-stage SDA model. Based on the above

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analysis, we applied structural decomposition analysis(SDA) approach to analyze the driving force

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of the CEs-PT transference.

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To get a general idea of SDA, we initially explored the CEs changes. Assume that there are

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two time periods for which input-output data are available. Using superscripts 0 and 1 for the two

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different years (0 earlier than 1, in our study, 1 express 2007 and 0 express 1997), our illustration

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of structural decomposition in an input-output model focused on the change of net CEs-PT

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outflow in two years. As usual, the net CEs-PT outflow in year t, n : (t = 0, 1), are found in an

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input-output system as

232 233 234 235

5 = 5; × 5!" and 5< = 5;< × 5!C  expresses the effect of related technology level change,

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reflects the technology driving force, 1/26= +6=C ∆8>  expresses the effect of the value

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transference demand change, reflects the demand driving force. Formula 7 is the first stage of

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SDA analysis model.

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Based on the first stage of SDA analysis, we carried out the second stage of SDA analysis.

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On the same principle, the change of net commodity value outflow in interprovincial trade can be

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demonstrated by the following formula:

∆5!" = 5!" − 5!: is a column matrix with 30 rows.

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2.3.3 Modeling for optimization—CR model. Focusing on the driving force of technology,

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demand, structure and scale, we built a coupling relationship (CR) model for analysis the change

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of ‘net commodity value outflow’ and ‘net CEs-PT outflow’ and the coupling relationship

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between them. Based on the CR model, we could find out the optimization targets and formulate

267

the optimization policy.

268 269 270 271 272 273

In the CR model, We used the ‘contribution rate changes’ to measure ‘net commodity value outflow change’ and ‘net CEs-PT outflow change’. Thus we obtained the following:

∆5L! = 5L! − 5L
(12)

Where ∆6QR and ∆6QS express the ‘contribution rate changes’ of ‘net commodity value

? outflow’ and ‘net CEs-PT outflow’, 8?T?UV = 8>? + 8? , means the sum of the commodity value ? outflow and inflow, ?T?UV = >? + ? , means the sum of the CEs-PT outflow and inflow. The 68?

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and 6? express the net commodity value outflow and the net CEs-PT outflow of industry W in

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year t, will be positive number for the net outflow industry, or the negative number for the net

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inflow industry.

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The whole application process of the CR model includes three steps:

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Firstly, based on the Formula (12), we calculated the contribution rate changes of net

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commodity value outflow (∆6QR ) and net CEs-PT outflow (∆6QS ) of each industry.

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Secondly, based on the coupling relationship of ∆nPY and∆nPZ , we divided industries into 4

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types and implemented different optimization policies for each industrial type according to the

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industrial characteristics. Specific description is as follows:

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If ∆6QR >0 and ∆6QS 0 and ∆6QS >0, the

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industry belongs to Type Ⅲ, characterized by net commodity value outflow has increased and net

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CEs-PT outflow has also increased from 1997 to 2007. In Type Ⅲ, if ∆6QR >∆6QS >0, the industry

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belongs to Type Ⅲ-A, characterized by net commodity value outflow has increased greater than

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net CEs-PT outflow increased; if ∆6QS >∆6QR >0, the industry belongs to Type Ⅲ-B, characterized

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by net commodity value outflow has increased less than net CEs-PT outflow increased. If ∆6QR 0;

460

and Type Ⅳ: ∆6QR