Economic Scale Effect Research Articles

Dynamic Relationships, Regional Differences, and Driving Mechanisms between Economic Development and Carbon Emissions from the Farming Industry: Empirical Evidence from Rural China

The coordinated development of the economy, resources, and environment is a key aspect of sustainable development. China’s rapid agricultural modernization has been accompanied by the continuous growth of rural economic aggregate and carbon emissions from the planting industry. However, the quantitative relationship between these two factors and its internal mechanism are not yet fully understood. In this paper, the Intergovernmental Panel on Climate Change (IPCC) method is used to calculate the carbon emissions of the planting industry in China from 1998–2019. Based on this, the Tapio decoupling analysis model was constructed to study the decoupling relationship between economic development and carbon emissions of the planting industry in China from 1998–2019 and the associated spatial and temporal evolution patterns. The effect of the complete decomposition model (without residuals), in terms of carbon emissions from the planting industry, on the process of economic development and its transmission mechanism are introduced. The results show that: (1) The carbon emissions of the planting industry in China increased with the economic development occurring from 1998–2005, where agricultural economic development was highly dependent on resource factors and the environment. The growth trend of carbon emissions of the planting industry slowed from 2006 to 2019, while economic development has gradually realized the decoupling of carbon emissions from the planting industry. (2) From 1998–2019, in Heilongjiang, Sichuan, and Hunan, the economic development was given priority, showing strong and negative decoupling with carbon emissions from farming. The economic development in most regions were given priority, showing strong decoupling with carbon emissions from farming. Up to 2019, decoupling was observed with a significant trend of spatial agglomeration. (3) Economic scale effects had a positive influence on the carbon emissions of the planting industry, while the technology effect and population effect had an inhibiting influence on the carbon emissions of the planting industry. The key policy implication of this paper is that improvement of the quality of economic development serves as the premise for the transformation of the economic development mode. It is necessary to reasonably regulate the economic growth rate and expansion scale, reduce resource consumption and pollutant emission technology, and to make full use of resources, in order to provide a basis for the formulation of reasonable emission reduction policies. An effective way to realize the sustainable development of the agricultural economy would be to improve the technical efficiency, control the population scale appropriately, and optimize the agricultural industrial structure.

Urbanization-induced spatio-temporal variation of water resources utilization in northwestern China: A spatial panel model based approach

Rapid urbanization has become a common occurrence worldwide, and it is important to ensure a sustainable water supply despite the increasing severity of water resource constraints. As an important area along the one belt and one road line, northwest China has experienced increasing water shortages during the rapid urbanization process in recent decades. In the current work, we employed the Spatial Panel Econometric Model to explore the spatial influence of urbanization on water resource utilization and its driving mechanism (e.g., economy, population, trade, capital, investment and industry structure) based on the water ecological footprint perspective during the period from 2005 to 2017. The results indicated that urbanization and the water ecological footprint showed a spatially clustered trend. In addition, the spatial lag regression coefficient of ρ in the spatial Durbin model was significant at the 1% level, showing that the water ecological footprint had an obvious spatial spill-over effect. Urbanization reduced the water ecological footprint in northwest China, and the main driving factors were the economic scale effect, population scale effect and investment pull effect. However, human capital exhibited a significantly positive relationship with the water ecological footprint, which was contrary to our theoretical hypothesis. In the classification of the water ecological footprint, urbanization reduced the agricultural water ecological footprint both temporally and spatially, while the other classifications all increased. The results are beneficial for informing policies towards sustainable water resource utilization under the framework of integrated development in the process of rapid urbanization in northwest China.

Study on Influencing Factors of Carbon Emission of Civil Buildings Based on Regional Differences

China's civil building carbon emissions show different characteristics due to different regions, so we should take measures to reduce carbon emissions according to local conditions. In this paper, LMDI model is used to study the influencing factors of carbon emissions in four regions of China according to the total amount and efficiency of carbon emissions. The influencing factors are divided into economic scale effect, per capita economic effect, urbanization effect, per capita building area effect, energy consumption per unit area effect and comprehensive carbon emission factor effect, and decompose each effect, find out the key factors that affect the carbon emissions of civil buildings in each region, and provide the direction for carbon emission governance of civil buildings in China.

Empirical Study on Environmental Pollution effect of FDI

This paper combs the relevant theories and literature, and analyses the three impact mechanisms of FDI on environmental pollution which includes economic scale, technology and structural effects , This paper selects foreign direct investment panel data of 29 provinces in China in 2009-2017, explores the impact of foreign direct investment on China’s environmental pollution, verifies that the “pollution shelter” hypothesis does exist in China, and concludes that the inflow of foreign direct investment aggravates China’s environmental pollution, and based on this, the paper puts forward targeted policy recommendations.

Does China’s carbon emissions trading scheme really work? A case study of the hubei pilot

China launched seven emissions trading scheme (ETS) pilots in 2011, and a national ETS, at the end of 2017 in an attempt to reduce its greenhouse gas emissions (GHGs) and drive its extensive energy transition in a cost-effective way. The Hubei province pilot ETS has operated since 2014, and an analysis of its effectiveness can provide lessons for the nascent national system. This paper disaggregates Hubei’s industrial carbon dioxide (CO2) emissions from 2005 to 2018 into economic scale, economic structure, energy efficiency, and energy structure effects using the logarithmic mean Divisia index (LMDI). Difference-in-difference (DID) models then assess the implementation effectiveness of this ETS, and its impact on the main influencing factors of the LMDI. Ten cities each in the Hubei and Hunan provinces are selected as treatment and control groups, respectively, in the DID models. Results show that the change in industrial CO2 emissions is mainly due to the economic scale and energy efficiency effects. After the ETS implementation, the energy structure effect has a limited impact on emissions, while the economic structure effect makes a negligible contribution. Moreover, the implementation of the ETS has had little demonstrable impact on industrial CO2 emissions, gross domestic product (GDP), energy consumption intensity, and CO2 emission intensity. This lacking of impact is caused by the GHG monitoring capacity deficits of the local Development and Reform Commission, and irrational quota allocations. Effective supervision and quota allocation should be developed to increase the impacts of the ETS.

Coal and economic development in Pakistan: A necessity of energy source

In this research, the logarithmic mean Divisia index (LMDI) and the Input-Output (I–O) models were used to explore the determinants of coal consumption change during 1999–2018 in Pakistan. Some novel factors industrial structure, energy intensity (EI), economic scale, energy mix, and personified coal consumption were chosen and added to the LMDI and I–O models. The aim is to estimate the factors’ effects of various sectors that might provide a new way for policy-makers. The results show: (1) the variations in the driving factors industrial, cement, Brick-kilns, and power sectors are growing. (2) The industrial structure effect increasing coal consumption significantly, while energy mix and EI are growing energy efficiency from 2014 to 2018. The economic scale effects played a major role in coal consumption during 1999–2018, while EI, industrial, and energy mix have mixed effects. (3) The economic scale is the only factor which is stable and increasing Pakistan’s coal consumption. (4) From the LMDI, the economic scale effect is stable while I–O expresses that EI, energy mix and a trend in the industrial structure effects are key motives in boosting in coal consumption. It is suggested coal reserve of 185.175 billion tons can significantly improve social, economic, and energy in Pakistan.

Decomposition Analysis of Influencing Factors of Electricity Consumption Demand and Intensity under Shanghai’s Transition Development

With the transition development of economic structure and energy consumption structure, electricity consumption of the production sector in China has been growing rapidly since the 21st century. The degree of electrification in Shanghai has deepened and electricity plays an increasingly important role in the agriculture, manufacturing and service sectors. This paper analyses the influential factors of Shanghai electricity consumption demand and intensity in the production sector during 1998 and 2017 through Index Decomposition Method. The results show that the energy structure effect has a growing contribution to the electricity consumption demand growth for the production sector in Shanghai since 2008, which implies that the electrification trend in Shanghai has deepened. The impact of energy efficiency improvement in Shanghai overtakes the impact of economic scale effect and energy structure effect, thus leading to electricity intensity decrease in the production sector. Besides, energy efficiency improvement in the manufacturing sector contributes most to power saving in Shanghai. Economic structure transition also contributes to electricity intensity decrease in Shanghai due to the decrease in the scale of the manufacturing sector. So, it is necessary to further tap the space for energy efficiency improvement in manufacturing and service industries and promote electrification when making strategic decisions of transition development, thus leading to green transformation and development of economy and energy.

Jurisdiction size and local government effectiveness: Assessing the effects of municipal amalgamations on performance

AbstractThe optimal jurisdiction size has been debated since Plato and Aristotle. A large literature has studied economic and democratic scale effects, but we have almost no knowledge of the effects of jurisdiction size on the effectiveness of local services. This is due to two methodological problems. First, selection bias and reverse causality often render change in jurisdiction size an endogenous variable. Second, there is a lack of empirical indicators of effectiveness, and most studies therefore focus on spending measures. Extant research thus studies economies of scale, leaving effectiveness of scale unexamined. We address both problems in a quasi‐experimental study of public schools. Our findings from the school area indicate that jurisdiction size does not have systematic effects on effectiveness. Our analysis therefore supports recent studies of economic and democratic scale effects that indicate that the search for the optimal jurisdiction size is futile.

Socioeconomic factors of industrial air pollutants in Zhejiang Province, China: Decoupling and Decomposition analysis.

In order to analyze and control air pollutant emissions effectively, on the basis of comprehensive consideration of three different pollution sources of industrial sulfur dioxide, industrial nitrogen oxides, and industrial smoke and dust, the Tapio decoupling model and LMDI decomposition model with six decomposition variables are constructed to compare the effects of socioeconomic factors on industrial air pollutant emissions in 11 cities in Zhejiang Province during 2006-2017. Then, a decoupling effort model is developed to analyze the effectiveness of the decoupling efforts taken at city level. This study found that (1) during the period of 2006-2017, the air pollutant emission reduction work in Zhejiang Province achieved remarkable results. More specifically, economic scale effect and population effect are the main factors for the increase of air pollutant emissions. And, the energy emission intensity effect and technological progress are the main driving forces for the reduction of three atmospheric pollutants, followed by the reduction effect of industrial structure and energy structure. (2) The environmental pollution problems of different air pollution sources in different cities are heterogeneous. (3) Eleven cities in Zhejiang Province have made significant decoupling efforts on the emission of three kinds of air pollutants, but there are some differences in the trend of the decoupling effort index of different pollution sources in different cities. In the future, illustrating by the example of Zhejiang, we should implement a "common but different" emission reduction strategy and emphasize pollutant emissions control during energy use in the efforts of further promoting the reduction of air pollutants.

Driving Forces Analysis of Power Consumption in Beijing Based on LMDI Decomposition Method and LEAP Model

With increasing pressure on resources and environment, sustainable development is becoming more and more important. As the largest energy consumer in the world, China needs to take measures to achieve energy transformation more urgently both from supply and demand side, which is of great significance for sustainable development and achieving carbon emissions target. In recent years, the capital city Beijing has also made great efforts to promote the replacement of electric energy in residential heating, manufacturing, transportation, power supply and consumption. In order to explore driving forces of total power consumption in Beijing`s final demand sectors, this paper decomposes the factors into industrial electricity substitution effect, industrial energy intensity effect, industrial structure effect, economic scale effect, population structure effect, residential electricity substitution effect, residential energy intensity effect and population size effect based on the logarithmic mean Divisia index (LMDI) decomposition method. The decomposition results show that the industrial electricity substitution effect made the largest contribution to increase power consumption in Beijing’s final energy consumption sector, followed by economic scale effect, residential energy intensity effect, population scale effect and residential electricity substitution effect, and other`s effect does the opposite. Finally, seven different scenarios are set up to forecast the future power consumption of Beijing`s final sectors based on the long-term energy alternative planning model (LEAP), which reveals the impact of energy efficiency improvement and electricity substitution polices on electricity consumption in Beijing`s final energy consumption sectors.

Identifying Driving Factors of Jiangsu's Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method.

The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th “Five-Year Plan” period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004–2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized.

Peak volume and time of energy-related CO2 emissions in Jiangsu Province, China based on long-range energy alternatives planning system model

China has put forward CO2 emissions reduction target (committing to achieve CO2 emissions peak or plateau by 2030) to prevent global climate change. With Jiangsu Province as a case, we explored whether China could achieve the 2030 CO2 emissions reduction target. We predicted the peak volume and time of CO2 emission in three scenarios, i.e., quick scenario, medium scenario, slow scenario, respectively, based on the long-range energy alternatives planning system (LEAP) model and the logarithmic mean Divisia index decomposition approach. The results showed that, during the period 2000 to 2015, the economic scale effect was the most important driver, whose contribution to the increase of total CO2 emissions was as high as 147.4%. The technology progress effect was the main mitigation factor for CO2 emissions, which caused CO2 emissions to decrease by 60.4%. In addition, the contributions of energy structure effect, industrial structure effect, per capita income effect and population scale effect to CO2 emissions were -5.3%, 9.7%, 11.0%, and 0.6% respectively. In quick and medium scenarios, the peak CO2 emissions of Jiangsu Province would be 701 million tons in 2025, and 795 million tons in 2029, respectively. In slow scenario, however, Jiangsu Province could not achieve the 2030 CO2 emissions reduction target. To achieve the 2030 target, Jiangsu Province needs to adopt some strategies, including actively developing the tertiary industry to balance the economic structure, continuously promoting energy saving and emissions reduction technologies to reduce energy consuming intensity, and vigorously deploying clean energy to optimize the energy consuming structure.

A comparison of decomposition the decoupling carbon emissions from economic growth in transport sector of selected provinces in eastern, central and western China

The transport sector is steadily becoming a new major source of carbon emissions in China. A better understanding of decoupling economic outputs from carbon emissions in the Chinese transport sector could facilitate the country's move to low-carbon transport. This study combines the Tapio decoupling model and Log Mean Divisia Index method to develop a decoupling effort model, in order to quantify the state of decoupling between economic outputs and carbon emissions, investigate the effects of carbon emissions, and evaluate these decoupling efforts in the transport sector of six provinces in eastern, central and western China. The results show that: (i) the eastern region had achieved stable decoupling; the development of the central and western regions was imbalanced, with frequent flux in their states of decoupling; (ii) the pattern of driving factor of transport carbon emissions in three eastern, central and western was similar. The economic scale effect was the primary contributor to carbon emissions, while the energy intensity effect was the main inhibitor. The influence of the carbon intensity effect and the population effect was found to be quite weak; (iii) the three economic regions had all undertaken efforts to achieve decoupling, with the eastern region ranking first in the extent of these efforts, and the western region ranking last; (iv) the energy intensity was a weak effect in decoupling effort, indicating that their energy utilization efficiency is not optimal, and that there still exists considerable room for improvement. Finally, some policy recommendations are proposed.

Driving factors of electric carbon productivity change based on regional and sectoral dimensions in China

In order to reduce carbon emissions and maintain economic development in the power industry of China, the improvement of electric carbon productivity (ECP) creates more efficiency. The carbon emission estimation in the power industry is significant to the ECP calculation in this study. Thus, to make the ECP calculation more reasonable and fair, this study estimates carbon emissions from the power industry by the consumption-based accounting principle considering power transfers among the provinces. In addition, this research analyzes seven driving factors of ECP change in China by exploring the time series decomposition (2003–2015) from the power consumption perspective. The Logarithmic Mean Divisia Index (LMDI) method takes the regional and departmental dimensions into consideration. By applying the data from 30 provinces (including province-level municipalities) and three industrial sectors between 2010 and 2015, the influencing factors of ECP in each province and each industrial sector are discussed. The results show that: 1) Regional and industrial sector ECP, per capita GDP are the driving factors of increasing ECP; conversely, environmental efficiency of power consumption, industrial structure effect from the perspective of power consumption, economic efficiency effect of power consumption, and the ratio of total population to electric CO2 emissions play leading roles in the decline of ECP. 2) From 2003 to 2015, there are four distinct stages of the ECP changes. 3) The sub-regional decomposition indicates, during 2010–2015, the main power exporters have higher or medium level ECP, and the main power importers with rapid economic development manifest lower or medium level ECP. 4) Instead of solely focusing on GDP, the Chinese government should pay more attention to increasing the economic and environmental efficiency of power utilization. 5) The sub-sector decomposition shows, during 2010–2015, although the economic and environmental efficiency of power utilization are negative in the secondary industry, they are the greatest among the three industrial sectors, and the influence of the economic scale effect is stronger than that of technological impact on different industrial sectors. Finally, several conclusions are obtained which might be useful for the central and local governments to improve the national and regional ECPs.

China’s international trade development and opening-up policy design over the past four decades

ABSTRACTChina’s opening-up in the past four decades has gone through three waves: the extensive margin of opening-up (1978–2001), the intensive margin of opening-up (2001–2017), and all-around opening-up (since 2017). This paper explores these three stages of the country’s economic reform. China’s gains from trade have been inspired by different economic factors. Before the turn of the century, the large trade volume was due to the realization of comparative advantage based on the country’s factor endowment. However, after its accession to the World Trade Organization, China’s gains from trade have been due, in large part, to the realization of economic scale effects associated with the larger market.

Economic Growth and Pollution Emission in China: Structural Path Analysis

The relationship between economic growth and environmental pollution has long been a controversial topic. However, simply the detection of the existence of environmental Kuznets curve (EKC) is not enough to understand how economic growth induced environmental pollution. This study investigated the path and mechanism of the effect of economic growth on the emission of two types of environmental pollutants, that is, industrial smoke and dust and sulfur dioxide, by using a structural equation model and a sample of 283 prefecture-level cities in China in 2005 and 2015. The research results show that economic growth exerted both direct and indirect effects on the emission of the two environmental pollutants. In addition to a direct impact through the economic scale effect, economic growth also indirectly impacted the two environmental pollutants emissions through three mediators, that is, industrial structure, technological innovations and environmental regulations. For different pollutants, the effect paths of economic growth on their emission showed both similarities and differences. First, with regards to industrial smoke and dust emissions and sulfur dioxide emissions, the effects of economic growth on the amount of these two emissions through environmental regulations and the industrial structure were negative inhibitory effects and positive promoting effects, respectively. This means that in prefectural-level cities in China, environmental regulation factors have produced some effects in reducing the emissions of these two pollutants while the industrial structure (level of industrialization) can increase the emissions of these two pollutants. However, the effect strength of these two paths shows a gradual weakening. Second, these two paths differ in effect strength and its changes. The positive promoting effects of the industrial structure on pollutant emission are significantly higher than the inhibitory effects of environmental regulation. In addition, our study also found that the direct impact path of economic growth on environmental pollution also passed significance testing, particularly in 2015. This shows that other reasons affect pollutant emission, such as system factors, spatial migration of industries and so forth.

Toward the Coordinated Sustainable Development of Urban Water Resource Use and Economic Growth: An Empirical Analysis of Tianjin City, China

The coordinated sustainable development of urban water resources and economic growth requires a better understanding of the relationship between industrial water use and economic growth. This study analyzed the relationship between urban industrial water use and economic development in Tianjin City in China (one of the four municipalities directly under the Central Government) from 2005 to 2015. The research combined the logarithmic mean Divisia index model with the Tapio model to develop a new decoupled model analyzing the relationship between urban industrial water use and economic development. The results show that: (1) Tianjin’s industrial water use and economic growth show a clear decoupling; (2) the economic scale effect drives Tianjin’s total industrial water use and economic growth towards weaker decoupling, stabilizing in a weakly decoupled state; and (3) the industrial structure effect and industrial water intensity effect drive the decoupling of industrial water use and economic growth in Tianjin. Finally, the paper provides policy recommendations to promote the decoupling of industrial water use and economic growth.

Examining the Factors Affecting Air Pollution Emission Growth in China

In this study, a structural decomposition method was applied to research the factors affecting the changes in air pollution emissions in China. Based on 1995–2009 data from the World IO Database, we combine China’s (Import) Noncompetitive IO Table and the Environmental Account Table. The results indicate that emission intensities represent the most important factor for reducing air pollution emissions in China. In contrast, economies of scale and the intermediate input product structure constitute the major causes for acceleration in the growth of air pollution emissions in China. From the perspective of final demand, the economic scale effect caused by investment demand is the main reason for this accelerated growth in China’s air pollution emissions in recent years. Consumption-driven economic growth is cleaner, while investment-driven economic growth is dirtier. This study constructed a structure decomposition model based on the input-output tables, which is suitable for studying the driving forces of various economic indicators, such as energy, carbon dioxide, and economic growth. At the same time, this method is helpful for analyzing the factors that influence changes in economic indicators that result from different economic pull modes, such as the final demand mode. However, the model does have limitations; for example, it does not consider the difference between general trade and processing trade in exports.

Local strategies for China's carbon mitigation: An investigation of Chinese city-level CO2 emissions

Abstract This paper provides a systematic analysis that identifies the driving forces of carbon dioxide (CO2) emissions of 286 Chinese prefecture-level cities in 2012. The regression analysis confirms the economic scale and structure effects on cities' CO2 emissions in China. If China's annual economic growth continues at the rate of 7%, CO2 emissions will increase by about 6% annually. In addition, climate conditions, urbanization and public investment in RD An increase in R&D investment by 1% can help reduce CO2 emissions by 0.21%. As cities in our study vary greatly based on their industry composition, development stage and geographical location, the patterns of their CO2 emissions are also variable. Our study improves the comprehensiveness and accuracy of previous carbon accounting method by distinguishing the scope 1 and scope 2 CO2 emissions and establishing a high spatial resolution dataset of CO2 emissions (CHRED). The analysis covers almost all Chinese prefectural cities and derives useful implications for China's low carbon development.

Productivity Growth Decomposition in the Manufacturing Industries of Food, Beverages, and Tobacco Products in India : A Stochastic Frontier Approach

The study applied stochastic production frontier approach to decompose the sources of total factor productivity growth (TFPG) of the organized manufacturing industries of food, beverages, and tobacco products in India into technological progress, changes in technical efficiency, economic scale effect, and allocative efficiency effect during the period from 1981-82 to 2010-11 during the entire period, during the pre- and post-reform period (1981-82 to 1990-91 and 1991-92 to 2010-11), and also, during two decades in the post-reform period (1991-92 to 2000-01 and 2001-02 to 2010-11) at the three-digit level of disaggregation of industries by NIC'04 and NIC'98. According to the estimated results, technological progress and allocative efficiency effect were the major contributors to TFPG of the organized manufacturing industries of food, beverages, and tobacco products in India during the study period from 1981-82 to 2010-11. Further, TFPG of the organized manufacturing industries of food, beverages, and tobacco products in India declined during the post-reform period (1991-92 to 2010-11), which is mainly accounted for by the decline in TP in this period, as our study revealed.