The Economy-carbon Nexus in China: a Multi-regional Input-output Analysis of the Influence of Sectoral and Regional Development

China has become the world's largest carbon dioxide (CO 2) emitter. Sectoral production activities promote economic development while also adding considerably to national CO 2 emissions. Due to their different sectoral structures, each region shows different levels of economic development and CO 2 emissions. The Chinese government hopes to achieve the dual objectives of economic growth and CO 2 emissions reduction by encouraging those sectors that have high economic influence and low environmental influence. Based on the above background, this study constructed an interregional sectoral economic influence coefficient (REIC) and a CO 2 emissions influence coefficient (RCIC) based on the basic multi-regional input-output (MRIO) model to analyse the economy-carbon nexus of 17 sectors in 30 regions in China in 2010. The results showed that most Chinese sectors and regions had low CO 2 emissions influences in 2010. However, some sectors showed negative environmental influences. Specifically, the mining-related sectors showed high CO 2 emissions influence with low economic influence. It is encouraging that some light industry and high-end equipment manufacturing sectors had low CO 2 emissions influence with high economic influence. For regions, geographic location and past preferential policies are the most important factors influencing local economic growth and CO 2 emissions reduction. Most inland regions have low economic influence with high or low CO 2 emissions influence. Meanwhile, most coastal regions showed high economic influence with low CO 2 emissions influence. Finally, we propose some policy implications for sectors and regions.


Introduction
The greenhouse effect is considered one of today's most important global environmental issues.CO 2 emissions from fossil fuel combustion are believed to be the most important factor contributing to greenhouse gas (GHG) and are responsible for over 60% of this deleterious effect [1].China has now become the world's largest CO 2 emitter [2][3][4][5][6][7].In 2015, China's CO 2 emission was 9.125 billion tons, accounting for 27.2% of the world's total emission [8].In 2015, at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change, the Chinese government declared that China would strive to fulfil its commitment to reaching peak CO 2 emissions by approximately 2030, by which point it also aims to reduce its CO 2 emissions per unit of GDP by 60% to 65% of its 2005 level [9].Sectoral production plays a leading role in China's CO 2 emissions [10].This is because production activities consume large amounts of energy and because China's energy consumption This study provides a theoretical framework to support policy-making for economic development and CO 2 reduction at the sectoral and regional levels.Specifically, we constructed two coefficients (REIC and RCIC) based on the MRIO model to capture the benchmarking sectors and regions with high economic influence and low CO 2 emissions influence in China.Moreover, based on these two coefficients, we propose policy implications for these sectors and regions to help the Chinese government achieve the dual objectives of economic growth and CO 2 emissions reduction.The paper proceeds as follows.In Section 2, we describe the methodology and data used to examine sectoral influences on the economy and CO 2 emissions.In Section 3, we present the results from the perspectives of sectors and regions.Finally, Section 4 concludes the study and provides policy implications.

Methodology
This study develops a methodology based on the MRIO model.We construct the REICs and RCICs of China's 17 sectors in 30 regions in 2010 based on the MRIO model.Based on the Leontief inverse coefficient, we calculated the REICs and RCICs to reflect economic performance and CO 2 emissions.The MRIO analysis can reflect inter-sectoral/inter-regional relevance in both a direct and indirect way, as well as the paths of influence on the economy and CO 2 emissions between sectors or regions.

MRIO Model
In this study, the MRIO table is presented in Table 1, where u rr ii represents the intermediate demand of the i th sector in the r th region supplied by the ith sector in the rth region; y rr ii represents the domestic final demand of the i th sector in the r th region supplied by the ith sector in the rth region, which normally covers rural household consumption, urban household consumption, government consumption, fixed capital formation and stock increases; and x r i represents the total output of the ith sector in the rth region.
The basic equation of the MRIO model can be expressed by Equation (1) [40,41,54]: where X denotes the total output, Y denotes the final demand, A denotes the technical coefficient and a rr ii is the element of matrix A.
When solved for total output X, this equation yields Equation (2): where, (I − A) −1 is the Leontief inverse matrix and I is the identity matrix.
As originally stated by Leontief, the inverse matrix can be expressed as follows: where, b rr ii is the element of matrix B. The REIC denotes the inter-regional sectoral economic influence coefficient.If the REIC is higher than 1, it shows that if the value-added of this sector increases by 1 unit, the value-added of the whole national economy increases by more than 1 unit.Thus, these sectors are considered high-REIC sectors and can have a great impact on national economic development.In contrast, if the REIC is lower than 1, it means that the value-added of this sector increases by 1 unit, but the value-added of the whole national economy increases by less than 1 unit.These sectors are considered low-REIC sectors, and they have a weak influence on national economic development.
The REICs are calculated by Equation ( 4): where, REIC r i refers to the economic influence of the increase in the value-added of the ith sector in the rth region on the increase in the value-added of all sectors in all other regions; b rr ii is the Leontief inverse coefficient of the ith sector in the rth region on the i th in the r th region; m is the number of regions and n is the number of sectors.In our study, m equals 30 and n equals 17.
2.1.3.Inter-Regional Sectoral CO 2 Influence Coefficient (RCIC) RCIC denotes the inter-regional sectoral CO 2 influence coefficient.If the RCIC higher than 1, it shows that the value-added of this sector increases by 1 unit and the level of national CO 2 emissions is leveraged by more than 1 unit.In contrast, if the RCIC is lower than 1, this indicates that the value-added of this sector increases by 1 unit and the level of national CO 2 emissions increases by less than 1 unit.
The RCICs are calculated by Equation ( 5): where, RCIC r i refers to the influence on CO 2 emissions of the increase in the value-added of the ith sector in the rth region on the increase in the CO 2 emissions of all sectors in all other regions; b rr ii is the Leontief inverse coefficient of the ith sector in the rth region to the i th in the r th region; m is the number of regions and n is the number of sectors.In our study, m equals 30 and n equals 17.
CO 2 productivity refers to the ratio of the value-added and CO 2 emissions in a certain period [27].CO 2 productivity is calculated by Equation (6): where, c p r i represents the CO 2 productivity of the ith sector in the rth region; V r i represents the value-added of the ith sector in the rth region; and C r i refers to the total amount of CO 2 emissions of the ith sector in the rth region.The results are shown in and Figures A1 and A2.
CO 2 emissions are estimated based on energy consumption and CO 2 emission factor (determined by fuel type) and are given by the following expression (Equation ( 7)) [28]: where, C r i represents the total amount of CO 2 emissions of the ith sector in the rth region, E r i,j is the energy consumption of the jth fuel type in the ith sector in the rth region, and F i,j is the CO 2 emissions factor of the jth fuel type in the ith sector.

The Mean Values of REIC and RCIC
This study calculates the REICs and the RCICs through the MRIO table of 17 sectors in 30 regions of China in 2010.To determine the key sectors and regions, we calculate the mean values of REIC and RCIC from the sectoral (Equation ( 8)) and regional perspectives (Equation ( 9)).
The REIC and RCIC of a sector are as follows: And the REIC and RCIC of a region is: where, the REIC i /RCIC i represent the mean values of REIC and RCIC of the ith sector in 30 regions.The REIC r /RCIC r represent the mean values of REIC and RCIC of the rth region.
According to Su and Ang [53], there are two import assumptions (i.e., competitive and non-competitive imports assumptions) in the MRIO model.The main difference between these two assumptions is that the former adopts the same production technology assumption in different regions and treats the imported products as the same as those produced domestically; while the latter considers the two types of products to be produced with different production technologies [53].Due to the different production technologies among countries, the estimated CO 2 emissions under the non-competitive imports assumption are usually smaller than those under the competitive imports assumption when estimating China's CO 2 emissions at the international level.Our paper focuses on China's CO 2 emissions at the domestic inter-regional level instead of at the international level.Furthermore, the differences in production technologies among regions within a country are smaller than those among different countries [50].Therefore, we adopted the competitive imports assumption.

Data
For the regional perspective, we defined two levels of spatial aggregation, L1 and L2 [55].The Chinese economy is treated as a single entity (L1) and is divided into 30 regions (L2), representing 30 provinces in China.The specific data are shown in Table A1.
For the sectoral perspective, Su et al. [56] assumed that there are n sectors in the SRIO table and m energy consumption sectors (n > m).They presented two data treatment schemes (Scheme 1 and Scheme 2) that could be used to make the sectors in the SRIO table compatible with the energy consumption data.Zhang et al. [50] offered another data treatment scheme in the MRIO table.Zhang et al. [50] noted that the main constraints of the sectoral level choice are that the sectors of different regions in the MRIO table are incompatible and that the number of aggregated sectors is determined by the common sectors of all regions.This paper uses the MRIO table to study CO 2 emissions at the interregional level within China.Therefore, we used the method that combines the advantage of Scheme 1 (referenced by Su et al. [56]) with the treatment in the MRIO model used by Zhang et al. [50] for aggregated sectors in the MRIO table.We adopt the 2010 MRIO table for China [57]; it includes 30 regions, each of which contains30 sectors in the original MRIO table.Moreover, there are 17 common sectors in these 30 regions in the MRIO table.The energy consumption data come from the China Energy Statistical Yearbook [58][59][60].Therefore, we aggregate 30 sectors in the MRIO table into 17 sectors to match the inter-regional trade table for 2010 (more detailed information is provided in Table A2).
Table 2 shows the emission factors used in our study.The emission factor of electricity generation is excluded for the following reasons.First, our study includes the electricity production and supply sector, which is included in the electricity, heat, gas and water production and supply sector (S14).This sector supplies an intermediate input of electricity to other sectors.Moreover, CO 2 emissions are estimated through the Leontief inverse coefficient, which contains both direct and indirect CO 2 emissions [13].When we calculate the total CO 2 emissions of a specific sector, we are including the direct CO 2 emissions from the fossil fuel combustion itself and the indirect CO 2 emissions from the intermediate electricity production input.Therefore, to avoid calculating CO 2 emissions repeatedly, we have used primary energy sources, including coal, coke, gasoline, kerosene, diesel oil, fuel oil, liquefied petroleum gases and natural gas, to calculate CO 2 emissions, and we have excluded electricity generation.The value-added and CO 2 emissions by sector and region are shown in Tables 3 and 4. The net calorific values (NCV) per unit of fuel and CO 2 emission factors (CEF) provided by the IPCC [61].

Results
According to Equations ( 8)-( 9), we obtain the results shown in Tables A3 and A4.To analyze the industrial influences on economic development and CO 2 emissions in the same framework, a matrix is constructed in which the x-axis represents REIC and the y-axis denotes RCIC.Thus, four quadrants have been formed to define four types of sectors or regions based on REIC and RCIC, including the HH-type, meaning high REIC with high RCIC; the HL-type, meaning high REIC with low RCIC; the LH-type, meaning low REIC with high RCIC; and the LL-type, meaning low REIC with low RCIC.

Sectoral Analysis
According to Figure 1, there are four HH-type sectors, two LH-type sectors, six LL-type sectors and five HL-type sectors.The average values of REIC and RCIC in these 17 sectors are 0.992 and 0.854.Generally, most sectors (11 out of 17) are in Quadrants III and IV, which indicates that the development of most sectors in China leads to relatively lower CO 2 emissions in 2010.This affirms that CO 2 emission reduction policies have functioned in most sectors.Regarding the effect of sectoral development on economic growth, about half of the 17 sectors have a relatively strong impact on the macro economy, while the other sectors play a relatively weak role in national economic growth.The top 3 REIC sectors are the transportation equipment manufacturing sector (S11), the construction sector (S15) and the textile and related products manufacturing sector (S4), with REICs of 1.133, 1.120 and 1.106, respectively.This means that if the value-added of these three sectors were to increase by 1 unit, China's total GDP would increase by 1.133, 1.120 and 1.106 units, respectively.The top 3 RCICs sectors are the electricity, heat, gas and water production and supply sector (S14), the metal smelting and products manufacturing sector (S9) and the non-metallic mineral products manufacturing sector (S8), with RCICs of 1.894, 1.673 and 1.559, respectively.If the value-added were to increase by 1 unit in these three sectors, total CO 2 emissions in China would increase by 1.894, 1.673 and 1.559 units, respectively.Quadrant I in Figure 1 shows the HH-type sectors, which include the electricity, heat, gas and water production and supply sector (S14), the metal smelting and products manufacturing sector (S9), the chemical industry sector (S7) and the papermaking and educational products manufacturing sector (S6).They have large positive effects on promoting national economic growth while also significantly reducing total CO2 emissions.The s of these sectors are 1.073 for S14, 1.022 for S9, 1.006 for S7 and 1.090 for S6, which are all higher than 1.000.This means that the effects of sectoral growth in these sectors on the national economy are stronger than the average level of the effects caused by all sectors.Due to a strong nexus with other sectors, the HH-type sectors are the key contributors to China's economic growth.However, accompanied by strong effects on national economic growth, their CO2 emissions also contribute to the increase in China's total CO2 emissions.The s are 1.894 for S14, 1.673 for S9, 1.279 for S7, and 1.159 for S6.Particularly, S14 and S9 have the highest s among all the sectors.A high in these sectors is related to high direct fossil energy consumption as well as high indirect consumption from other sectors with which they share a nexus.For example, coal consumption in 2010 for S14 was 1525.72 million tons, accounting for 48.86% of total national coal consumption; S7 not only has high coal consumption (15% of the national total) but also strongly influences the consumption of petroleum products and electricity [59,60].
Quadrant II in Figure 1 shows the LH-type sectors, including the non-metallic mineral products manufacturing sector (S8) and the mining sector (S2).Similar to the HH-type sectors, S8 and S2 also have high s, with values of 1.559 and 1.497, respectively, due to their high consumption of fossil energy and a strong nexus with fossil energy-intensive sectors such as the electricity, heat, gas and water production and supply sector (S14).However, the s of S8 and S2 are lower than 1.000, at only 0.974 and 0.919, respectively.Because of their small effect on national economic growth but large effect on national CO2 emissions, the LH-type sectors' development can be considered restrictive.
Compared to the HH-type sectors, the LL-type sectors in Quadrant III in Figure 1 have low s and s.The LL-type sectors include the following sectors: the food and tobacco Quadrant I in Figure 1 shows the HH-type sectors, which include the electricity, heat, gas and water production and supply sector (S14), the metal smelting and products manufacturing sector (S9), the chemical industry sector (S7) and the papermaking and educational products manufacturing sector (S6).They have large positive effects on promoting national economic growth while also significantly reducing total CO 2 emissions.The REICs of these sectors are 1.073 for S14, 1.022 for S9, 1.006 for S7 and 1.090 for S6, which are all higher than 1.000.This means that the effects of sectoral growth in these sectors on the national economy are stronger than the average level of the effects caused by all sectors.Due to a strong nexus with other sectors, the HH-type sectors are the key contributors to China's economic growth.However, accompanied by strong effects on national economic growth, their CO 2 emissions also contribute to the increase in China's total CO 2 emissions.The RCICs are 1.894 for S14, 1.673 for S9, 1.279 for S7, and 1.159 for S6.Particularly, S14 and S9 have the highest RCICs among all the sectors.A high RCIC in these sectors is related to high direct fossil energy consumption as well as high indirect consumption from other sectors with which they share a nexus.For example, coal consumption in 2010 for S14 was 1525.72 million tons, accounting for 48.86% of total national coal consumption; S7 not only has high coal consumption (15% of the national total) but also strongly influences the consumption of petroleum products and electricity [59,60].
Quadrant II in Figure 1 shows the LH-type sectors, including the non-metallic mineral products manufacturing sector (S8) and the mining sector (S2).Similar to the HH-type sectors, S8 and S2 also have high RCICs, with values of 1.559 and 1.497, respectively, due to their high consumption of fossil energy and a strong nexus with fossil energy-intensive sectors such as the electricity, heat, gas and water production and supply sector (S14).However, the REICs of S8 and S2 are lower than 1.000, at only 0.974 and 0.919, respectively.Because of their small effect on national economic growth but large effect on national CO 2 emissions, the LH-type sectors' development can be considered restrictive.
Compared to the HH-type sectors, the LL-type sectors in Quadrant III in Figure 1 have low REICs and RCICs.The LL-type sectors include the following sectors: the food and tobacco processing sector (S3), the commercial and transport service sector (S16), the other manufacturing sector (S13), the electric and electronic equipment manufacturing sector (S12), the agriculture sector (S1), and the other services sector (S17).Given their current energy saving and emissions reduction goals, these sectors can be encouraged to a certain extent.The low REIC in these sectors is due to their weak nexus with other sectors in China.For example, S1 is an upstream sector that promotes some sectors' development well but has little effect on other sectors' growth.Furthermore, S3 is strongly linked to S1 with a weak REIC, which leads to the low REIC in S3.The low REIC in S12 is mostly due to the fragmented industry chain.For instance, although China is rich in rare earths, which are important inputs for electric and electronic products, a large portion of products using processed rare earths are imported from foreign countries such as Japan [62].Therefore, S12 has a weak nexus with the domestic raw material sectors but strongly depends on foreign imports.
The HL-type sectors in Quadrant IV in Figure 1 are most worthy of attention.These sectors have strong positive influences on the whole economy while also maintaining low CO 2 emissions; they include the textile and related products manufacturing sector (S4), the timber processing and furniture manufacturing sector (S5), the machinery industry sector (S10), the transportation equipment manufacturing sector (S11), and the construction sector (S15).Especially, S11, S15 and S4 rank as the top 3 among all sectors in the REIC, while they have low RCICs of 0.445, 0.209 and 0.819, respectively.These sectors are consistent with China's development expectations for the near future, namely promoting economic development with low emissions.It is slightly surprising that the construction sector (S15) is a HL-type sector because most studies have indicated that it is a carbon-intensive sector [63].This is because the estimate of RCIC for the whole economy is based on the inverse matrix of the matrix of CO 2 productivity.Therefore, if a sector has higher CO 2 productivity compared to the average CO 2 productivity of all sectors, this sector would have a lower RCIC.The CO 2 productivity of the construction sector (S15) was 106.36 thousand CNY per ton at the current price in 2010, while the average carbon productivity was 51.63 thousand CNY per ton at the current price in 2010.Therefore, China's construction sector (S15) has the low RCIC of 0.209.However, the REIC and RCIC here only provide an average for the sectors.Each sector in different areas performs differently in both REIC and RCIC; this is analyzed in Section 3.2.

Regional Analysis
Similar to sectoral performance, most regions (22 regions) in China also have low RCIC, according to Figure 2.There is one HH-type region, seven LH-type regions, eleven LL-type regions and eleven HL-type regions.The average values of REIC and RCIC in these 30 regions are 1.000 and 0.833.The top 3 regions in terms of REIC are Shandong (R15), at 1.188; Beijing (R1), at 1.123; and Zhejiang (R11), at 1.110.The top 3 regions in terms of RCIC are Guizhou (R24), Yunnan (R25) and Jilin (R7).The RCICs of these three regions are 1.154, 1.337 and 1.348, respectively.
Within the HH-type region, only Guangdong (R19) has high REIC and RCIC simultaneously, as shown in Figure 2a; the REIC and RCIC equal 1.043 and 1.312, respectively Guangdong (R19) plays a very important role in supporting the national economy; however, this entails a huge amount of CO 2 emissions.As shown in the map (a) in Figure 2, Guangdong (R19) is located in the southern coastal area that was one of the first regions to open up in China.Due to its proximity to the special administrative regions of Hong Kong and Macao, Guangdong (R19) has a high level of openness [64].The development of the goods trade and the export-oriented processing industry has promoted economic growth in Guangdong.In 2010, Guangdong's (R19) GDP reached 4.6 billion Yuan at the current price, accounting for 11% of the national total [64].At the sectoral level, there are 12 sectors (S3, S4, S5, S6, S7, S8, S9, S10, S11, S13, S14, S15) out of the total of 17 sectors with high REICs above 1.000.At the same time, regarding the effect on CO 2 emissions, 7 sectors (S3, S5, S8, S9, S10, S13, S14) in Guangdong (R19) have high RCICs above 1.000.This means that 40% of the 17 sectors in Guangdong (R19) are HH-type sectors, which makes Guangdong an HH-type region.
Figure 2b shows the LH-type regions, including Guizhou (R24), Yunan (R25), Jilin (R7), Inner Mongolia (R5), Guangxi (R20), Qinghai (R28) and Ningxia (R29).In these regions, the REIC is lower than 1, while the RCIC is higher than 1.These regions have different locations, as shown in map (b) in Figure 2: Guangxi (R20) is located in the south, Guizhou (R24) and Yunnan (R25) are located in the southwest, Qinghai (R28) and Ningxia (R29) are located in the northwest, Inner Mongolia (R5) is located in the north, and Jilin (R7) is located in the northeast.However, they all share strong industry and weak service.The REICs of the other services sectors (S17) in these regions are in the range of 0.594 to 0.772.However, the REICs of most industry sectors in these regions are approximately 1.000.Most of these regions have rich mineral resources, and mining and mineral products processing are their key sectors.According to sectoral analysis, the mining sector (S2) is a LH-type sector.This may be one of the main reasons that these regions belong to the LH-type.
Figure 2c represents the LL-type regions, which include Hunan (R18), Shanxi (R4), Sichuan (R23), Xinjiang (R30), Shaanxi (R26), Gansu (R27), Chongqing (R22), Hubei (R17), Hainan (R21), Fujian (R13), and Heilongjiang (R8).For the LL-type regions, both the REIC and the RCIC are lower than 1.Similar to the LH-type regions, most of the LL-type regions are located in the inland area, where it is difficult to attract capital such as foreign direct investment (FDI) and excellent human resources.In these regions, the light industry sectors have had a relatively better economic effect.
The HL-type regions are shown in Figure 2d; they include Shandong (R15), Liaoning (R6), Henan (R16), Anhui (R12), Beijing (R1), Hebei (R3), Jiangsu (R10), Shanghai (R9), Zhejiang (R11), Tianjin (R2), and Jiangxi (R14).Most of these regions are located in eastern coastal China, as shown in map (d) in Figure 2.They have superior trading ports, benefit from many policies supporting economic development, and attract a large amount of capital and talent.In these regions, the industrial structure is reasonable and has a balance of industry and service sectors.Moreover, the REICs in most sectors in the HL-type regions are higher than 1.Therefore, these regions promote national economic development significantly.Due to the concentration of capital and talent, production technology has improved rapidly, which not only boosts production efficiency but also improves CO 2 productivity.For example, although Zhejiang (R11) ranks third in REIC, its RCIC was only 0.380.
Energies 2017, 10, 93 11 of 28 in Figure 2: Guangxi (R20) is located in the south, Guizhou (R24) and Yunnan (R25) are located in the southwest, Qinghai (R28) and Ningxia (R29) are located in the northwest, Inner Mongolia (R5) is located in the north, and Jilin (R7) is located in the northeast.However, they all share strong industry and weak service.The of the other services sectors (S17) in these regions are in the range of 0.594 to 0.772.However, the s of most industry sectors in these regions are approximately 1.000.Most of these regions have rich mineral resources, and mining and mineral products processing are their key sectors.According to sectoral analysis, the mining sector (S2) is a LH-type sector.This may be one of the main reasons that these regions belong to the LH-type.
Figure 2c represents the LL-type regions, which include Hunan (R18), Shanxi (R4), Sichuan (R23), Xinjiang (R30), Shaanxi (R26), Gansu (R27), Chongqing (R22), Hubei (R17), Hainan (R21), Fujian (R13), and Heilongjiang (R8).For the LL-type regions, both the and the are lower than 1.Similar to the LH-type regions, most of the LL-type regions are located in the inland area, where it is difficult to attract capital such as foreign direct investment (FDI) and excellent human resources.In these regions, the light industry sectors have had a relatively better economic effect.
The HL-type regions are shown in Figure 2d; they include Shandong (R15), Liaoning (R6), Henan (R16), Anhui (R12), Beijing (R1), Hebei (R3), Jiangsu (R10), Shanghai (R9), Zhejiang (R11), Tianjin (R2), and Jiangxi (R14).Most of these regions are located in eastern coastal China, as shown in map (d) in Figure 2.They have superior trading ports, benefit from many policies supporting economic development, and attract a large amount of capital and talent.In these regions, the industrial structure is reasonable and has a balance of industry and service sectors.Moreover, the s in most sectors in the HL-type regions are higher than 1.Therefore, these regions promote national economic development significantly.Due to the concentration of capital and talent, production technology has improved rapidly, which not only boosts production efficiency but also improves CO2 productivity.For example, although Zhejiang (R11) ranks third in , its was only 0.380.

Sectoral-Regional Integrated Economy-Carbon Nexus Analysis
To determine the regular distribution of economic sectors, we observed the economy-carbon nexus from both regional and sectoral perspectives (shown in a-q).
For the agriculture sector (S1) and the other services sector (S17), the S1 and S17 in all regions are scattered in the LL-type (Quadrant III).This means that the S1 and S17 in all regions exhibit low economic influence and low CO 2 emissions influence on other sectors.Therefore, S1 and S17 in all regions should focus on improving their economic influence to transition to the HL-type.
For the mining sector (S2), 21 regions out of 30 regions are scattered in the LH-type (Quadrant II) and the LL-type (Quadrant III).This means that S2 in most regions shows low economic influence.However, the influences of CO 2 emissions are different in these 21 regions due to their different energy structures.In addition, the other 3 regions, which include Zhejiang (R11), Jiangxi (R14) and Hainan (R21), belong to the HL-type.Therefore, the energy consumption structures, production technology and industrial chain constructions in these 3 regions can be used as benchmarks for other regions.
For the food and tobacco processing sector (S3), the transportation equipment manufacturing sector (S12) and the electrical and electronic equipment manufacturing sector (S13), most regions are concentrated in the LL-type (Quadrant III) and the HL-type (Quadrant IV).This means that when promoting economic development, S3, S12 and S13 in most regions of China have relatively low CO 2 emissions influences.However, their levels of economic influence are different because production technology and industrial structures are different in these regions.Moreover, S3 in 8 regions, S12 in 8 regions and S13 in 12 regions belong to the HL-type, and these regions can be used as benchmarks for other regions.
For the textile and related products manufacturing sector (S4), the mechanical industry sector (S10) and the transportation equipment manufacturing sector (S11), most regions are concentrated in the HL-type (Quadrant IV).For the above three sectors, S4 in 22 regions, S10 in 21 regions and S11 in 23 regions belong to the HL-type.Generally, the development of S4, S10 and S11 in China show high economic influence and low CO 2 emissions influence, though there are some regions scattered in the LL-type, HH-type or LH-type.Therefore, S4, S10 and S11 can be considered the priority sectors that that should be supported to promote China's economic development in the future.
For the papermaking and educational products manufacturing sector (S6), 24 regions are concentrated in the HH-type (Quadrant I) and in the HL-type (Quadrant IV).This indicates that S6hasstrongeconomic influence in most regions.Out of these 24 regions, there are 14 regions and 10 regions distributed in the HH-type and HL-type, respectively.This means that S6 in most regions should focus on reducing its CO 2 emission influences.
For the chemical industry sector (S7), the non-metallic mineral products manufacturing sector (S8), the metal smelting and products manufacturing sector (S9) and the electricity, heat, gas and water production and supply sector (S14), most regions are scattered in the HH-type (Quadrant I) and LH-type (Quadrant II).This illustrates that these 4 sectors in most regions show high CO 2 emissions influence.It is exciting that S7 in 8 regions, S8 in 3 regions, S9 in 7 regions and S14 in 6 regions belong to the HL-type, which can be the benchmark for other regions.
For the construction sector (S15), it is slightly surprising that S15 in all regions is scattered in the HL-type (Quadrant IV).This indicates that S15 has a strong economic influence on both the downstream and upstream sectors.Moreover, in all regions S15 shows low CO 2 emission influences.Similarly, Meng et al. [13] studied China's CO 2 emissionsin17 sectors in 2002 and 2007 and found that construction accounted for just 0.8% of CO 2 emissions in 2002 (ranking 10th out of all 17 sectors) and 0.6% of CO 2 emissions in 2007 (ranking 12th out of all 17 sectors).This may be because the estimate of RCICs for the whole economy is based on the Leontief inverse coefficient and CO 2 productivity.Therefore, S15 has a lower CO 2 emissions influence due to its larger CO 2 productivity compared to the average of CO 2 productivities for all sectors.
For the commerce and transportation service sector (S16), there are 24 regions scattered in the LL-type (Quadrant III).Moreover, there is no HH-type region in S16.This means that S16 in most regions has low CO 2 emissions influence and low economic influence.It is exciting that in two regions (Beijing (R1) and Shanghai (R9)); S16 belongs to the HL-type and can be the benchmark for other regions.

Concluding Remarks
Currently, China faces the dual pressures of economic growth and CO 2 emissions reduction at both the national and local levels.In this study, a MRIO model is used to determine benchmark sectors and notable patterns of regional development for policy-makers.Holistic policy implications are depicted in Figure 3, in which the LH-type, HH-type and LL-type sectors or regions should be transitioned to the HL-type because the HL-type produces a high economic influence coefficient and a low CO 2 emissions influence coefficient, which is the ideal benchmark.and CO2 productivity.Therefore, S15 has a lower CO2 emissions influence due to its larger CO2 productivity compared to the average of CO2 productivities for all sectors.
For the commerce and transportation service sector (S16), there are 24 regions scattered in the LL-type (Quadrant III).Moreover, there is no HH-type region in S16.This means that S16 in most regions has low CO2 emissions influence and low economic influence.It is exciting that in two regions (Beijing (R1) and Shanghai (R9)); S16 belongs to the HL-type and can be the benchmark for other regions.

Concluding Remarks
Currently, China faces the dual pressures of economic growth and CO2 emissions reduction at both the national and local levels.In this study, a MRIO model is used to determine benchmark sectors and notable patterns of regional development for policy-makers.Holistic policy implications are depicted in Figure 3, in which the LH-type, HH-type and LL-type sectors or regions should be transitioned to the HL-type because the HL-type produces a high economic influence coefficient and a low CO2 emissions influence coefficient, which is the ideal benchmark.At the sectoral level, specific policy implications mainly focus on promoting economic influence and reducing CO2 emissions influence.
The HL-type sectors are benchmarking sectors because of their high economic influence and low CO2 emissions influence; they should maintain their current development and continue to enhance their economic influence while reducing their CO2 emissions.
For the sectors with low economic influence, more economic policy implications should be considered.For example, the Chinese government can extend and upgrade the industrial chain in the same region and to the other regions and tighten the relationship between upstream and downstream to enhance the sector's economic influence.
For the sectors with high CO2 emissions influence, more attention should be focused on how to reduce CO2 emissions in these sectors' production activities.Specific policy implications include improving energy efficiencies through improved production technology and adjusting the energy consumption structure, especially by enhancing the proportion of clean energy derived from, e.g., wind power and solar power.Moreover, raising the cost of CO2 emissions by implementing carbon taxes and carbon emissions trading is also a useful method because it can force the transition of both the energy consumption structure and improvements to energy efficiency.At the sectoral level, specific policy implications mainly focus on promoting economic influence and reducing CO 2 emissions influence.
The HL-type sectors are benchmarking sectors because of their high economic influence and low CO 2 emissions influence; they should maintain their current development and continue to enhance their economic influence while reducing their CO 2 emissions.
For the sectors with low economic influence, more economic policy implications should be considered.For example, the Chinese government can extend and upgrade the industrial chain in the same region and to the other regions and tighten the relationship between upstream and downstream to enhance the sector's economic influence.
For the sectors with high CO 2 emissions influence, more attention should be focused on how to reduce CO 2 emissions in these sectors' production activities.Specific policy implications include improving energy efficiencies through improved production technology and adjusting the energy consumption structure, especially by enhancing the proportion of clean energy derived from, e.g., wind power and solar power.Moreover, raising the cost of CO 2 emissions by implementing carbon taxes and carbon emissions trading is also a useful method because it can force the transition of both the energy consumption structure and improvements to energy efficiency.At the regional level, both geographic location and preferential policies of the past determine, to a large extent, whether a region can become the HL-type.
Energies 2017, 10, 93 14 of 28 The HH-type region of Guangdong exploit the advantages of its geographical location and early opening-up, acquire the advanced production technology of foreign countries, and adjust its energy structure to reduce its CO 2 emissions.
For the inland regions belonging to the LH-type and the LL-type, the most important measures include constructing transportation infrastructure and attracting capital and talent to enhance these regions' economic influence in the national economy.Moreover, these regions should strengthen communications with the HL-type regions to improve production technology and further reduce their CO 2 emissions.Specifically, the LH-type regions should increase the value-added proportion of HL-type sectors in regional GDP.

Figure 1 .
Figure 1.Matrix of the and of China's 17 sectors in 2010.

Figure 1 .
Figure 1.Matrix of the REIC and RCIC of China's 17 sectors in 2010.

Figure 2 .
Figure 2. Map of different types of regions in China in 2010.(a) HH-type regions; (b) LH-type regions; (c) LL-type regions; (d) HL-type regions

Figure 3 .
Figure 3. Policy implications for different types of sectors or regions.

Figure 3 .
Figure 3. Policy implications for different types of sectors or regions.

Table 1 .
The MRIO table for China in 2010.

Table 2 .
CO 2 emissions per unit of fuel combustion in China in 2010.

Table 3 .
The value-added and CO 2 emission of China's 17 sectors in 2010.

Table 4 .
The value-added and CO 2 emission of China's 30 regions in 2010.

Table A2 .
Sectors in this study.

Table A3 .
and of China's 17 sectors in 2010.

Table A3 .
and of China's 17 sectors in 2010.
Figure A2.Mean values of CO 2 productivity of China's 30 regions in 2010.

Table A4 .
REIC and RCIC of China's 30 regions in 2010..398R24 0.934 1.554 R10 1.023 0.429 R25 0.973 1.377 R11 1.110 0.380 R26 0.934 0.914 R12 1.038 0.580 R27 0.922 0.895 R13 0.998 0.600 R28 0.967 1.055 R14 1.091 0.314 R29 0.955 1.032 R15 1.188 0.966 R30 0.923 0.931 30 regions; (d) Economic and CO 2 emission performance of the textile and related products manufacturing sector (S4) in China's 30 regions; (e) Economic and CO 2 emission performance of the timber processing and furniture manufacturing sector (S5) in China's 30 regions; (f) Economic and CO 2 emission performance of the papermaking and educational products manufacturing sector (S6) in China's 30 regions; (g) Economic and CO 2 emission performance of the chemical industry sector (S7) in China's 30 regions; (h) Economic and CO 2 emission performance of the non-metallic mineral products manufacturing sector (S8) in China's 30 regions; (i) Economic and CO 2 emission performance of the metal smelting and products manufacturing sector (S9) in China's 30 regions; (j) Economic and CO 2 emission performance of the mechanical industry sector (S10) in China's 30 regions; (k) Economic and CO 2 emission performance of the transportation equipment manufacturing sector (S11) in China's 30 regions; (l) Economic and CO 2 emission performance of the electrical and electronic equipment manufacturing sector (S12) in China's 30 regions; (m) Economic and CO 2 emission performance of the other manufacturing sector (S13) in China's 30 regions; (n) Economic and CO 2 emission performance of the electricity, heat, gas and water production and supply sector (S14) in China's 30 regions; (o) Economic and CO 2 emission performance of the construction sector (S15) in China's 30 regions; (p) Economic and CO 2 emission performance of the commerce and transportation service sector (S16) in China's 30 regions; (q) Economic and CO 2 emission performance of the other services sector (S17) in China's 30 regions.