Economic Scale Effect Research Articles

Decoupling analysis of economic development and human well-being: A case study of the Qinghai-Xizang Plateau, China.

Clarifying the relationship between economic development and human well-being is conducive to promoting high-quality economic development. This study focused on 16 prefecture-level cities in the Qinghai-Xizang Plateau region. The critic weighting method assessed the 2007-2018 human well-being index (HWI). The Tapio decoupling model allowed the study of the human well-being decoupling state. Finally, the drivers of decoupling between economic development and human well-being were analyzed using the Logarithmic Mean Divisia Index method. The results indicated that (1) almost all cities in the study region had an upward 2007-2018 HWI trend, but there were significant differences in growth magnitude and change trend. (2) Economic development and human well-being in the study region in 2007-2018 had expansion negative decoupling, thus, human well-being increased with economic growth, but not as fast as gross domestic product. 9 cities in 2007 showed weak decoupling, expansion connection, and expansion negative decoupling, increasing to 13 cities by 2018, indicating that human well-being development gradually improved from 2007 to 2018. (3) For most cities, the economic scale effect was the most influential factor in the decoupling of economic development and human well-being. Therefore, this study provided policy recommendations for decoupling economic development and human well-being.

Decoupling relationship between logistics growth and carbon emissions and driving factors in Chongqing: A novel decomposition framework

A comprehensive understanding of the logistics decoupling status and driving factors is of immense theoretical and practical importance for rationally formulating low-carbon logistics policies and accelerating the realization of high-quality development. Present study introduces the logarithmic mean Divisia index method (LMDI) decomposition technique into Tapio index model, extends traditional decoupling model, and establishes a new analytical framework for the decoupling relationship between logistics growth and carbon emissions. Using long-term data from Chongqing (1997–2021), this study investigated the Chongqing logistics decoupling relationship and driving factors. The study found the following: (1) Chongqing logistics carbon emissions show phased changes and face greater pressure to reduce emissions. (2) The decoupling status of logistics growth and carbon emissions is predominantly expansive and weak decoupling, with an overall evolutionary trend of “expansive decoupling–weak decoupling–strong decoupling. The average decoupling index in 2013–2021 was 0.5523, indicating a decreasing trend; however, there was still a large gap in realizing a strong decoupling goal. (3) The energy consumption intensity effect facilitates logistics carbon decoupling, the economic scale effect has a strong inhibiting effect, and the industrial structure effect and transportation intensity effect are staged. Finally, targeted policy recommendations are proposed to expedite logistics carbon emissions decoupling in Chongqing.

Spatiotemporal Evolution Characteristics and Influencing Factors of Industrial Carbon Emissions in the Yellow River Basin

Scientific assessment of industrial carbon emissions in the Yellow River Basin and identification of its influencing factors are of great importance for promoting green transformation, ecological protection, and high-quality development of the Yellow River Basin. Considering nine provinces in the Yellow River Basin as the research objects； using relevant data on industrial development and energy consumption in the Yellow River Basin from 2000 to 2019； and with the help of IPCC carbon emission measurement, spatial autocorrelation, and LMDI decomposition, the spatial and temporal evolution characteristics and influencing factors of carbon emissions from industries and industrial sectors in the Yellow River Basin were analyzed. Reasonable suggestions were put forward for reducing the carbon emissions from industries in the Yellow River Basin. The results showed that： ① From 2000 to 2019, industrial carbon emissions in the Yellow River Basin showed a fluctuating growth trend, with a decreasing growth rate. The spatial pattern changed from "low in the upstream and high in the middle and downstream" to "high and low value distribution," and the spatial difference gradually expanded. ② The high carbon industry was the most important source of industrial carbon emissions in the Yellow River Basin, accounting for 96.35% of the carbon emissions between the industries with a continuous growth trend, which was a significant difference. The middle and low carbon industry carbon emissions and the total proportion was low, showing different fluctuations； nine provinces and nine industrial industries had significant spatial variability. ③ Energy structure intensity, economic scale, and population scale promoted the increase in industrial carbon emissions in the Yellow River Basin and energy consumption intensity had an inhibitory effect on the increase in carbon emissions. The economic scale effect was positive and significant, which offset the negative effect of energy consumption intensity. Spatial variability was observed in the contribution value of the influence effect of the factors affecting the carbon emissions of the industry in nine provinces.

Drivers of industrial carbon emissions in the Yangtze River Delta region, China: A combination of decoupling and LMDI models

ABSTRACT The expansion of the industrial economy has resulted in high carbon emissions (CE) from the Yangtze River Delta (YRD) region. Industrial CE reduction is a non-negligible way to achieve green and low-carbon development. In this paper, we aim to study the decoupling effect of industrial CE and its socio-economic influencing factors in the YRD region from 2005 to 2020. First, the decoupling relationship between industrial CE and industrial output in the YRD region is measured. Then, using the LMDI method, the variation of industrial CE is decomposed into six factors: energy structure ( Δ c s ), energy intensity ( Δ c i ), economic scale ( Δ c e ), labor structure ( Δ c l ), urbanization rate ( Δ c u ) and population size ( Δ c p ) effect. Subsequently, by combining with the decoupling and LMDI models, the contribution of each factor to the decoupling of industrial CE is studied, and the elasticity of each factor is expressed by η s , η i , η e , η l , η u , and η p . The main findings show that: (1) There are two states of weak and strong decoupling between industrial CE and the industrial economy in the YRD. (2) Δ c e is the most powerful factor in promoting industrial CE, while Δ c i and Δ c l are the factors that inhibit industrial CE in the YRD. (3) η i and η l have promoted the decoupling of industrial CE from economic advancement in the YRD and its three provinces and one city, while η e has consistently been the largest obstacle to the decoupling process. Finally, the policy recommendations proposed in this paper aim to help policy-makers and practitioners in the YRD region achieve a win-win balance between industrial CE and industrial output, and inspire further research on the decoupling and decomposition of industrial CE from an academic perspective.

Decomposition analysis of regional differences in China's carbon emissions based on socio-economic factors

Developing policies and setting carbon reduction targets for various regions in China are important actions to narrow the gap of regional carbon emissions. This study reports on the differences in carbon emissions in the eastern, central, and western regions of China from 2005 to 2021 using the Theil index, and decomposes carbon emissions into emission intensity, industrial structure, economic scale, and population size effects using the Logarithmic Mean Divisia Index. In order to analyze the impact of socio-economic factors on carbon emissions, the industrial structure and emission intensity effects are divided into six socio-economic sectors. The major outcomes reported that: (1) the regional differences account for 70 % of the total carbon emissions difference, and the eastern region reported the most significant carbon emission difference; (2) the emission intensity and the economy scale are the top two effects on carbon emissions. The economic scale effect always drives an increase in carbon emissions, while emission intensity promotes a decrease in carbon emissions. The industrial structure effect shows a gradually decreasing trend in all regions, especially in the eastern region, the effect has turned negative since 2013. This is due to the proposed industrial transformation and upgrading in China since 2013, which has promoted the reconstruction of competitive advantages for high polluting heavy industry enterprises and accelerating the green transformation of industrial technology; (3) the industry sector contributes to the highest carbon emissions, followed by the transportation sector. Both sectors are the top two emitters and should be targeted for carbon reduction. The carbon emissions were generated based on the terminal energy consumption. The contribution effects of multiple industries and the differences in carbon emissions as affected by the driving factors and economic mechanisms among different regions in China were analyzed.

Analysis of the Decoupling State and Driving Effects of Economic Development and Production Water Use in Jiangsu Province, China

Identifying the evolutionary patterns and drivers of regional water use is a prerequisite for achieving the strictest water resource management system. This study used the Tapio-LMDI model to analyze the decoupling state and driving factors between economic development and production water use in Jiangsu Province from 2004 to 2020. The results show that: (1) From the evolution of the water use structure, the total water use in Jiangsu Province shows a decreasing trend from 2004 to 2020. Among them, the production water use decreased by 9.59%. From the characteristics of economic development (constant prices), the growth of Jiangsu’s gross regional product (GDP) from 2004 to 2020 reached 363%. (2) In terms of the decoupling status, economic development and production water use in Jiangsu Province underwent a “weak decoupling—strongly decoupling” transition and achieved “strongly decoupling” in 2020, with a decoupling elasticity coefficient of −2.30. (3) From the perspective of the decoupling drivers, the reduction in production effects has contributed to the decoupling between economic growth and water use in Jiangsu Province. By sector, the decline in the water use intensity effect and the industrial structure effect in the primary and secondary sectors were the main reason for the decline in its water use, while the increase in the industrial structure effect and economic scale effect of the tertiary sector has effectively contributed to the increase in water use in the tertiary sector. Therefore, there is an urgent need to improve the water use efficiency of the primary and secondary sectors, accelerate the transformation and upgrading of the tertiary sector, and realize a “strongly decoupling” pattern between economic development and production water use in Jiangsu Province.

Does central ecological transfer payment enhance local environmental performance? Quasi-experimental evidence from China

Central transfer payment for the key ecosystem zones has become the most funded payments for ecosystem services program in China. The ecological fiscal transfer (EFT) aims to improve the eco-environmental quality of the targeted counties by providing supplementary fiscal funds, though not strictly specifying the usage. This study employs a time-varying difference-in-difference strategy to investigate the impact of EFT on environmental outcome by using a comprehensive county-level dataset covering 1949 counties from 2004 to 2015. Estimates show that the policy had a positive impact on local air quality, whereas the dynamic effect is diminishing. The policy effect is achieved through more stringent environmental regulation and stricter industrial access. Reduction of industrial dust emission is more noticeable than that of sulfur dioxide. There is an economic scale effect that the scale of a county can increase effectiveness of EFT on air quality improvement, whereas an opposite development stage effect will undermine its effectiveness. Heterogeneities were detected concerning transfer intensity, intervention period, geographical region, and ecosystem type. These findings facilitate a deep understanding how EFT could play a significant role in protecting local environment in large developing countries. Several policy implications are drawn to enhance the effectiveness of the largest EFT program.

Spatiotemporal patterns and drivers of carbon emissions from municipal solid waste treatment in China

Reducing carbon emissions from municipal solid waste (MSW) treatment is non-negligible for China to meet its “carbon peaking and carbon neutrality” targets. It is critical to objectively evaluate the spatiotemporal patterns and drivers of carbon emissions from MSW treatment. This study estimates the carbon emissions from MSW treatment across 30 Chinese provinces from 2011 to 2020. The joint approach LMDI-PDA model is further used to refine the impact of policy on carbon emission changes from technical and efficiency perspectives, while considering the socio-economic factors. The results showed that carbon emissions from MSW treatment grew significantly until peaking at 202.05Mt CO2e in 2017 and then stabilized, finally dropping to 165.10 Mt CO2e in 2020 due to the impact of COVID-19. Compared with the “12th Five-Year Plan” period, the MSW emissions intensity declined significantly during the “13th Five-Year Plan” period, indicating the effective implementation of waste emission control measures. Furthermore, the slowdown in the growth of national emissions was primarily driven by technological advances in waste treatment. Technical efficiency change effect, MSW generation intensity effect, economic scale effect, and population scale effect impeded national emissions decline. Since the performance of various drivers varied greatly in different provinces, a cluster analysis was conducted to provide policy recommendations in provinces with similar characteristics. Both the methods and results of this study can provide better decision-making support for national and provincial carbon emissions control policies targeting MSW treatment.

The environmental effects of undertaking industrial transfer in developing countries: a quasi-natural experimental evidence in China.

The construction of National Industrial Relocation Demonstration Zones (NIRDZ) is important for China's industrial transfer, but its environmental influence cannot be neglected. This study explores the environmental effects of industrial transfers by studying China's NIRDZ. By employing panel data of 284 cities in China between 2005 and 2019, we compare environmental quality changes over time in areas with and without demonstration zones based on the staggered difference-in-differences (DD) technique. The results demonstrate a 0.032 increase in the environmental quality level of industrial receivers after the implementation of demonstration zones. The effect of demonstration zones on environmental improvement is moderated by natural resources, capital accumulation, and technological innovation capabilities. This impact is more fully realized in cities with resource-based, low-capital accumulation, and high-tech innovation but is not statistically significant difference at various levels of human resources. The environmental improvement effect of the NIRDZ is powerful in central cities and small- and medium-sized cities instead of western regions and large-scale cities. Additionally, mediation analysis is adopted to assess the potential mechanism between the association of NIRDZ and the environment. The demonstration area negatively affects environmental quality through the economic scale effect while improving environmental quality through the technological innovation effect. We provide empirical evidence that the NIRDZ is positively correlated with the environment and identify the technology effect as one underlying driver of this correlation to help developing countries address its detrimental impacts.

Formation of trade networks by economies of scale and product differentiation

Understanding the structure and formation of networks is a central topic in complexity science. Economic networks are formed by decisions of individual agents and thus not properly described by established random graph models. In this article, we establish a model for the emergence of trade networks that is based on rational decisions of individual agents. The model incorporates key drivers for the emergence of trade, comparative advantage and economic scale effects, but also the heterogeneity of agents and the transportation or transaction costs. Numerical simulations show three macroscopically different regimes of the emerging trade networks. Depending on the specific transportation costs and the heterogeneity of individual preferences, we find centralized production with a star-like trade network, distributed production with all-to-all trading or local production and no trade. Using methods from statistical mechanics, we provide an analytic theory of the transitions between these regimes and estimates for critical parameters values.

유럽통합은 무역의 중력을 어떻게 변화시키는가?

Purpose – This study examines whether the influence of the gravity model after economic integration is strengthened or weakened by variables through trade volume between the EU and eurozone member states. Design/Methodology/Approach – We analyzed the effect of economic integration of the EU and the Eurozone through the gravity equation, the result generally supports the basic assumptions of a gravity model. Findings – As economic integration goes deeper, the trade-creation effect becomes larger. In particular, in the case of the Eurozone, which even achieved monetary integration, the trade-creation effect increased dramatically compared to the intra-regional trade of EU member states. In European economic integration, the economic scale effect to increase trade volume weakened, and the distance effect, which decreases trade volume, became stronger. Research Implications – When economic integration deepens, the trade volume between member countries increases dramatically, and it can be especially advantageous when member countries are geographically close, or are located in the center of other membership countries. However, trade volume decreased by the distance effect is smaller compared to the degree of trade creation within economic integration, and it was found that the European integration acts favorably on trade creation for its member countries.

The Carbon Emissions Effect of China’s OFDI on Countries along the “Belt and Road”

With the continuous practice of the “Belt and Road” initiative, the countries along the “Belt and Road” have achieved rapid social and economic development. However, environmental problems have become increasingly prominent. Around the world, there are comments that China’s “Belt and Road” initiative is a result of resource plundering, transfer of backward production capacity, and environmental degradation of countries along the line. This study quantitatively evaluated the static, dynamic, linear, and non-linear effects of China’s foreign direct investment on the carbon emissions of countries along the line. The results showed that: (1) The direct effect of China’s foreign direct investment on the carbon emissions of countries along the route was significantly negative. (2) The economic scale and industrial structure effects of China’s foreign direct investment increased the carbon emissions of countries along the route. The production technology effect suppressed the carbon emissions of countries along the route and played a leading role. (3) The estimation results of the system generalized method of moments showed that the carbon emissions of countries along the route were significantly affected by the lag period, but the impact was small. (4) The results of the threshold regressive model showed that the GDP and proportion of industrial added value had significant threshold effects on the carbon emissions effect of China’s outward foreign direct investment. When the GDP of countries along the route exceeded 7.2696, China’s outward foreign direct investment carbon emissions reduction effect could not be realized; when the proportion of the industrial added value of countries along the route was lower than 4.0106, China’s outward foreign direct investment carbon emission reduction effect could not be realized. Based on the research conclusion, we concluded that China and countries along the “Belt and Road” should strengthen cooperation on carbon emissions reduction, jointly promote low-carbon construction of industrial parks, accelerate cooperation on green energy projects, and establish a green development fund to achieve sustainable development of the countries along the “Belt and Road”.

Driving factors and key emission reduction paths of Xinjiang industries carbon emissions: An industry chain perspective

As a typical resource-based region, Xinjiang is under increasing pressure to reduce carbon emissions. The ability to identify the drivers of carbon emissions in the industry and mapping out key emission reduction paths are core components of achieving carbon emission reduction in Xinjiang. This research incorporates energy and environmental factors, constructs a hybrid input–output model of “energy-environment-economy,” and uses the environmental input–output structural decomposition (EIO-SDA) and structural path decomposition (SPD) methods to analyze the drivers of carbon emissions from energy consumption and key emission reduction paths in Xinjiang. First, it shows that the economic-scale effect and the energy-intensity effect are the biggest facilitators of carbon emissions and most significant barriers to carbon emission reduction in Xinjiang. Second, capital formation and domestic trade are the primary sources of demand driving changes in carbon emissions in Xinjiang. Third, sectors involving the production and supply of electricity and heat, such as heavy manufacturing and energy industries, including petroleum processing, coking, nuclear, fuel processing, and chemical industries, are the key sectors responsible for carbon emission reduction in Xinjiang. Last, in terms of industry chains, “metal smelting and rolling processing industry/non-metallic mineral products industry—construction industry—fixed capital formation” and “oil and gas extraction industry (S20)—(intermediate sector)—final demand” are the most important paths driving the growth and decline of carbon dioxide (CO2) emissions from Xinjiang industries, respectively. To promote Xinjiang's carbon emission reduction targets, the Xinjiang government should actively improve its energy structure, promote a change in demand growth patterns, and formulate comprehensive management policies for high-carbon-transfer industries according to an industry chain perspective.

Analysis of the impact of the Beijing-Tianjin-Hebei coordinated development on environmental pollution and its mechanism.

Taking detailed rules for implementing of the plan on atmospheric pollution prevention and control in Beijing-Tianjin-Hebei region and surrounding areas promulgated and implemented in 2013 as the starting point, taking air pollution as the research object, using the propensity score matching method and difference-in-differences method to answer whether coordinated development of Beijing-Tianjin-Hebei is conducive to resolving the problem of air pollution. Using panel data from 251 cities in China 2003-2016, this paper discusses the environmental effects and mechanisms of coordinated urban cluster development. The results showed that the coordinated development of Beijing-Tianjin-Hebei had a significant and continuous effect on the convergence of PM2.5 to low levels. The results of further mechanism identification show that the policy of atmospheric joint defense rules in the process of Beijing-Tianjin-Hebei coordinated development mainly promotes the convergence of PM2.5 through population and economic scale effect, structural effect and technical effect, and promotes the convergence of PM2.5 in the low direction by reducing the economic growth scale of the city itself. It is suggested to further promote the formation and development of Beijing-Tianjin-Hebei integration, strengthen cooperation among local governments in environmental governance e across regions, and promote the green growth and coordinated development of urban clusters.

Temporal-spatial determinants of renewable energy penetration in electricity production: Evidence from EU countries

The challenge of global warming requires deep decarbonization of the electricity system, which can be achieved with a higher share of renewables in the electricity mix. However, the driving mechanism for renewable penetration in electricity production has not been clearly revealed so far. Using the data of the European Union (EU) during 2001–2017, this paper aims to investigate the spatial-temporal heterogeneity and dynamic evolution mechanism of renewable electricity penetration. To this end, from the investment-production-consumption perspective, renewable electricity generation is decomposed into seven factors through temporal and spatial factor decomposition methods. Then, this paper integrates the factor decomposition results into a coupling elasticity analysis to study the dynamic evolution mechanism of renewable penetration in electricity production. The results are as follows. (1) Investment structure contributes the most to increasing renewable electricity generation, while the energy intensity effect and renewable generation-to-capacity ratio effect are the main reasons for the reduction in renewable electricity generation. The effect of technological structure is minor. (2) The economic scale effect and the investment structure effect are dominating factors accounting for the regional difference of renewable power generation between the EU countries and the benchmark region. (3) The progress of renewable electricity penetration accelerates over time for the EU, western EU, and eastern EU. The Western EU has realized remarkable progress in facilitating renewable electricity penetration during 2009–2017. (4) On the whole, the investment effect makes the largest contribution to promoting renewable electricity penetration, while the production effect is the major inhibiting factor, showing that there is considerable potential in renewable generation efficiency improvement in the EU. This paper provides insightful implications for energy policies aimed at the shift towards a low-carbon electricity system in the EU, which also gives a reference for other economies.

A comparison analysis of the decoupling carbon emissions from economic growth in three industries of Heilongjiang province in China.

Growing environmental pressure urges China to develop in a sustainable and low carbon way, and thus China strives to achieve a carbon peak by 2030 and carbon neutrality by 2060. As the main energy and heavy industry based in China, carbon emissions in Heilongjiang Provice play a crucial role in achieving China's climate change targets. Compared with countries with similar populations, Heilongjiang Province (HLJP)'s carbon emissions are still at a low level. In the research on Heilongjiang Province's carbon emissions among three industries, we apply Tapio decoupling and the Logarithmic Mean Divisia Index (LMDI) method to analyze the decoupling elasticity and effort index of three industries' growth and carbon emissions in HLJP from 2005 to 2017. Moreover, Deng's grey relation model is adopted to judge the relation degree between industries and carbon emissions. The results show that: (1) with respect to decoupling, the secondary industry is in a relatively stable state of decoupling; however, the decoupling state of the primary industry is not stable at all, which appears expansive or negative in the studies over the past years. (2) The carbon emissions of three industries show an upward trend during the study period, of which the economic scale effect contributes most, while the energy intensity was the main inhibitor to carbon emissions. (3) Three industrial-economic structures are related to carbon emissions at a high level. The tertiary industry ranks the first, which is followed by the secondary industry and then the primary industry. The economic growth of the tertiary industry is much faster than the other two, hence, its relation degree is the highest. Overall, our analysis can provide an effective reference for the decline of carbon emissions in each industry.

Carbon dioxide emissions and Chinese OFDI: From the perspective of carbon neutrality targets and environmental management of home country

Studies investigating the relationship between foreign direct investment (FDI) and the environment have focused predominantly on the effects of FDI on host country environments with less attention paid to the impact on home countries. This study turns its attention to the effects of outward foreign direct investment (OFDI) on a home country's carbon dioxide emissions. We use three paths through which OFDI can affect the carbon dioxide emissions of a home country, including economic scale, technology level, and industry composition effects. Using a simultaneous equation model and panel data of 30 provinces of China for the period of 2003–2017, this study finds that OFDI is positively related to the carbon dioxide emissions of the home country, though the effects of emissions have weakened dynamically due the technology developments brought about by OFDI. More specifically, we find that both ‘pollution haven’ and ‘pollution halo’ effects existing in three different paths. The paths of industry composition and technology level show negative effects on carbon dioxide emissions, whilst the path of economic scale is positive. OFDI is also found to be negatively related to the carbon intensity of the home country.

How can Germany reduce production-based and consumption-based carbon emissions? A decomposition analysis

Developing more effective mitigation strategy to achieve the carbon reduction targets set by the Paris Climate Agreement requires to more comprehension of the driving factors of the decline in carbon emissions. Production-based and consumption-based carbon emission in Germany declined by 2.8% and 8.5% from 2000 to 2015, respectively. In this work, the key drivers behind decline in carbon emission in Germany is explored by combing the multi-regional input-output and structural decomposition analysis. The results show German carbon emissions has been undergoing structural changes. Although its production-based and consumption-based emission declined, German carbon footprint embodied in exports increased, and its share in the production-based carbon footprint also increased. Germany's embodied emission exports to emerging countries increased, although Germany's embodied carbon exports remained mainly concentrate in developed countries, especially in EU (over 40%) and USA (∼10%). The decomposition results revealed that economic scale effect was the leading contributor to increase in carbon footprint in Germany's trade, followed by the industrial structure effect of final demand. The effects of production structure on German's exports was less than its imports. The carbon intensity coefficient was the most significant factor of decreased carbon emission. Generally, the decreasing effect of carbon intensity coefficient exceeded the increasing effect of economic scale, which lead to carbon reduction. The example of Germany shows that improving carbon emission efficiency and optimizing production structure can reduce carbon emission without lowering economic growth.

Identifying the driving factors of water consumption from water-energy-food nexus in the Yangtze River Delta region, China.

The current water shortage in China is critical. Moreover, the water shortage has become the main bottleneck hindering sustainable economic growth. Against the background of China's dual control target of total water use and intensity, we choose the Yangtze River Delta (YRD) region as a research object, which encompasses Shanghai, Jiangsu, Zhejiang, and Anhui. Based on the perspective of water-energy-food nexus, we employ the generalized Divisia index method to decompose the change of water use into eight factors, regional economic scale effect, regional energy use scale effect, regional food production effect, regional water technology effect, regional energy technology effect, regional water-energy nexus effect, regional food-energy nexus effect, and regional water technology for food production effect, and analyze the contribution of each driver to identify the key drivers of total water use control. The results showed that the top four influencing factors are GDP, water intensity, energy consumption, and water-energy nexus in the YRD region. Regional economic scale is the first driving factor for increasing water use in the YRD region except for Zhejiang. Water intensity is the primary driving force of water-saving in Zhejiang. Energy consumption is the third driver of increasing water use in Jiangsu and Anhui. The effect of water-energy nexus is the third driving factor that affects the change of water use in Shanghai and Zhejiang. The authorities in the YRD region should vigorously develop water and energy utilization technologies to increase the water intensity and decrease energy consumption. The energy sector should decrease the water use to decrease the water-energy nexus which is also the main driving factor affecting the change of water use.

Social and Economic Factors of Industrial Carbon Dioxide in China: From the Perspective of Spatiotemporal Transition

The reduction of CO2 emission has become one of the significant tasks to control climate change in China. This study employs Exploratory Spatial Data Analysis (ESDA) to identify the provinces in China with different types of spatiotemporal transition, and applies the Logarithmic Mean Divisia Index (LMDI) to analyze the influencing factors of industrial CO2 emissions. Spatial autocorrelation of provincial industrial CO2 emissions from 2003 to 2017 has been demonstrated. The results are as follows: (1) 30 provinces in China are categorized into 8 types of spatiotemporal transition, among which 24 provinces are characterized by stable spatial structure and 6 provinces show significant spatiotemporal transition; (2) For all types of spatiotemporal transition, economic scale effect is mostly contributed to industrial CO2 emission, while energy intensity effect is the most crucial driving force to reduce industrial carbon dioxide emission; (3) provinces of type HH-HH, HL-HL and HL-HH are most vital for CO2 emission reduction, while the potential CO2 emission increase of developing provinces in LL-LL, LH-LH and LL-LH should also be taken into account. Specific measures for CO2 emission reduction are suggested accordingly.