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At present, China is transforming into a green development mode in all respects, and improving green energy efficiency is a key component of this transformation. Using panel data of 2011–2018, this research adopts the Super-SBM (Slack-Based Model) to calculate the green energy efficiencies of China’s 29 provinces and a GML (Global Malmquist-Luenberger) index method to explain the efficiency changes. Empirical analysis draws the following conclusions: 1) China’s green energy efficiency presented a slowly decreasing rather than increasing trend. 2) Technological progress was a major factor in efficiency improvement. However, its contribution was canceled by energy overuse. 3) Provinces with low green energy efficiency tend to geographically gather in the regions with rich energy resource endowment. Instead, provinces with high green energy efficiency are relatively geographically scattered, and most of them are China’s most developed regions. 4) Green energy efficiencies among China’s four major regions have significant differences. Generally, the mean level is east > northeast > west > central. 5) The key policy directions to improve China’s green energy efficiency include using transfer payment to balance the regional development, breaking down the barriers among provinces to facilitate energy circulation, and refining energy price structure to mitigate rebound effects.
Ming Meng; Danlei Qu. Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis. Energy 2021, 121912 .
AMA StyleMing Meng, Danlei Qu. Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis. Energy. 2021; ():121912.
Chicago/Turabian StyleMing Meng; Danlei Qu. 2021. "Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis." Energy , no. : 121912.
The Beijing-Tianjin-Hebei (BTH) region is an important economic center of China but has the problem of severe environmental pollution. Urbanization prompts the household energy consumption growth and then deteriorates the environmental conditions in this region. Based on panel data of the BTH region from 2000 to 2017, this research uses a dynamic threshold model with per capita disposable income as the threshold variable to investigate the non-linear impact of urbanization on household energy consumption. The empirical results show that: 1) In the process of urbanization, per capita resident disposable income has a significant threshold effect on household energy consumption, and their relationship presents an inverted U-shaped trend. 2) The change of household energy consumption habits can significantly improve the household energy consumption level. 3) Industrial structure adjustment, urban population density, and education level of residents have restraining effects on household energy consumption growth. 4) Technical advance and GDP per capita growth opposite effects. The empirical analysis also shows that the regional government can consider increasing the disposable income of the residents in Hebei Province to alleviate the imbalance in energy consumption. At the same time, efforts should be made to develop high-tech industries and high-end service industries, and adopt a more intensive urban planning development model to increase urban population density.
Ming Meng; Jin Zhou. Threshold Effect of Urbanization Level on Household Energy Consumption in Beijing-Tianjin-Hebei Region. Polish Journal of Environmental Studies 2021, 30, 4069 -4083.
AMA StyleMing Meng, Jin Zhou. Threshold Effect of Urbanization Level on Household Energy Consumption in Beijing-Tianjin-Hebei Region. Polish Journal of Environmental Studies. 2021; 30 (5):4069-4083.
Chicago/Turabian StyleMing Meng; Jin Zhou. 2021. "Threshold Effect of Urbanization Level on Household Energy Consumption in Beijing-Tianjin-Hebei Region." Polish Journal of Environmental Studies 30, no. 5: 4069-4083.
China’s transportation industry has become one of the major industries with rapid growth in CO2 emissions, which has a significant impact in controlling the increase of CO2 emissions. Therefore, it is extremely necessary to use a hybrid trend extrapolation model to project the future carbon dioxide emissions of China. On account of the Intergovernmental Panel on Climate Change (IPCC) inventory method of carbon accounting, this paper applied the Logarithmic Mean Divisia Index (LMDI) model to study the factors affected by CO2 emissions. The affected factors are further subdivided into the scale of employees, per capita carrying capacity, transport intensity, average transportation distance, energy input and output structure, energy intensity and industrial structure. The results are as follows: (1) Per capita carrying capacity is the most important factor to promote the growth of CO2 emissions, while industrial structure is the main reason to inhibit the growth of CO2 emissions; (2) the expansion of the number of employees has played a positive role in the growth of CO2 emissions and the organization and technology management of the transportation industry should be strengthened; (3) comprehensive transportation development strategy can make the transportation intensity effect effectively reduce CO2 emissions; (4) the CO2 emissions of the transportation industry will continue to increase during 2018–2025, with a cumulative value of about 336.11 million tons. The purpose of this study is to provide scientific guidance for the government’s emission reduction measures in the transportation industry. In addition, there are still some deficiencies in the study of its influencing factors in this paper and further improvements are necessary for the subsequent research expansion.
Ming Meng; Manyu Li. Decomposition Analysis and Trend Prediction of CO2 Emissions in China’s Transportation Industry. Sustainability 2020, 12, 2596 .
AMA StyleMing Meng, Manyu Li. Decomposition Analysis and Trend Prediction of CO2 Emissions in China’s Transportation Industry. Sustainability. 2020; 12 (7):2596.
Chicago/Turabian StyleMing Meng; Manyu Li. 2020. "Decomposition Analysis and Trend Prediction of CO2 Emissions in China’s Transportation Industry." Sustainability 12, no. 7: 2596.
North China is one of the country’s most important socio-economic centers, but its severe air pollution is a huge concern. In this region, precisely forecasting the daily photovoltaic power generation in winter is essential to improve equipment utilization rate and mitigate effects of power system on the environment. Considering the climatic characteristics of North China, the winter days are divided into three classifications. A forecasting model based on random forest algorithm is then designed for each classification. To evaluate its performance, the proposed model and three other methods are separately used to forecast the daily power generation at the Zhonghe PV station, which is located in the center of North China. Empirical results show that, because of its ability to reduce the risk of overfitting by balancing decision trees, the proposed model obtains mean absolute percentage errors as low as 2.83% and 3.89% for clear and cloudy days, respectively. For days in which weather conditions are unusual, forecasting errors are relatively large. On these days, enlarging training samples, performing subdivision, and imposing manual intervention can improve the forecasting precision. Generally, the proposed model is better than the other three methods for nearly all error evaluation indicators in each classification.
Ming Meng; Chenge Song. Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter. Sustainability 2020, 12, 2247 .
AMA StyleMing Meng, Chenge Song. Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter. Sustainability. 2020; 12 (6):2247.
Chicago/Turabian StyleMing Meng; Chenge Song. 2020. "Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter." Sustainability 12, no. 6: 2247.
The rapid growth of household electricity consumption is threatening the sustainable development of China’s economy and environment because of its impacts on the operation efficiency of the electric power system. To recognize the driving factors of the consumption growth and offer policy implications, based on the consumption-related data of 2015 and 2016, this research used the rank sum ratio (RSR) method to divide China’s 30 provinces into 5 groups and a logarithmic mean Divisia index (LMDI) algorithm to decompose the composition growth of each group into the quantitative contribution of each driving factor. The following conclusions were drawn from the empirical analysis. (1) The Yangtze basin is the most vigorous region of consumption growth and should be principally monitored. (2) Climate conditions have a remarkable impact on consumption growth and should be a key consideration when making differentiated household electricity policies. (3) The rebound effect has already appeared in a few of the most developed regions. Electricity price is an effective measure in dealing with this effect. (4) The improvement of the income level is the most important driving factor for consumption growth. (5) For provinces with high growth vitality, the change in the burden level of electricity expenditure prompts consumption growth. However, for provinces with low growth vitality, the situations are opposite. (6) The impacts of electricity price and population on consumption growth are negligible.
Ming Meng; Shucheng Wu; Jin Zhou; Xinfang Wang. What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis. Sustainability 2019, 11, 4648 .
AMA StyleMing Meng, Shucheng Wu, Jin Zhou, Xinfang Wang. What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis. Sustainability. 2019; 11 (17):4648.
Chicago/Turabian StyleMing Meng; Shucheng Wu; Jin Zhou; Xinfang Wang. 2019. "What is Currently Driving the Growth of China’s Household Electricity Consumption? A Clustering and Decomposition Analysis." Sustainability 11, no. 17: 4648.
The Beijing–Tianjin–Hebei (BTH) region is a top urban agglomeration of China but has the problem of severe environmental pollution. Most of the current researches on the sustainable development of this region only concentrate on the environmental pollution itself and ignore its relationship to the socioeconomic development. In this research, an entropy-based coupling model, a polynomial equation with partial least squares algorithm, and socioeconomic and environmental data in 2006–2015 were used to measure and fit the above relationship. Empirical analysis led to the following conclusions. (1) Beijing, Tianjin, and Hebei presented similar socioeconomic development modes but different environmental pollution modes. (2) The social economy of the BTH region has been developing at the expense of environmental pollution, but the environmental cost has been decreasing year by year. (3) At present, the BTH region has huge potential to improve its environment. (4) Increasing the investment in the treatment of industrial pollution in Tianjin and mitigating the soot (dust) emissions in Tianjin and Hebei are the major environmental policy directions. (5) Controlling the development of smelting and pressing of ferrous metals and other building material sectors in Hebei is the major economic policy direction.
Ming Meng; Tingting Pang; Liguo Fan. Measuring and Fitting the Relationship between Socioeconomic Development and Environmental Pollution: A Case of Beijing–Tianjin–Hebei Region, China. Discrete Dynamics in Nature and Society 2019, 2019, 1 -10.
AMA StyleMing Meng, Tingting Pang, Liguo Fan. Measuring and Fitting the Relationship between Socioeconomic Development and Environmental Pollution: A Case of Beijing–Tianjin–Hebei Region, China. Discrete Dynamics in Nature and Society. 2019; 2019 ():1-10.
Chicago/Turabian StyleMing Meng; Tingting Pang; Liguo Fan. 2019. "Measuring and Fitting the Relationship between Socioeconomic Development and Environmental Pollution: A Case of Beijing–Tianjin–Hebei Region, China." Discrete Dynamics in Nature and Society 2019, no. : 1-10.
In the present “new normal” economic mode, the household is a major driver of China's electricity consumption growth. To guide the development of the electric power industry in adapting to this situation, this study used the household electricity consumption and population data of 30 provinces during 2001–2014, a three-dimensional decomposition model, and a hybrid trend extrapolation model to explore the driving factors of China's household electricity consumption growth and forecast its future development trend before 2030. Empirical analysis drew the following conclusions: (1) China's household electricity consumption growth is mainly attributed to the improvement of its living standards and still has great potential. (2) Population increase and provincial population structure adjustment have little impact on household electricity consumption growth. (3) In 2030, China's household electricity consumption per capita will increase to 1.06 thousand kWh per capita. (4) China's household electricity consumption will increase to 1.57 trillion kWh in 2030, which is twice that in 2015. The implementation of the universal two-child population policy will have no significant impact on these forecasting results. (5) Raising household electric price level, setting cool and heat storage price, and developing the micro-grid are the suggested policy directions.
Ming Meng; Lixue Wang; Wei Shang. Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models. Energy 2018, 165, 143 -152.
AMA StyleMing Meng, Lixue Wang, Wei Shang. Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models. Energy. 2018; 165 ():143-152.
Chicago/Turabian StyleMing Meng; Lixue Wang; Wei Shang. 2018. "Decomposition and forecasting analysis of China's household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models." Energy 165, no. : 143-152.
As an essential measure to mitigate the CO2 emissions, China is constructing a nationwide carbon emission trading (CET) market. The electric power industry is the first sector that will be introduced into this market, but the quota allocation scheme, as the key foundation of market transactions, is still undetermined. This research employed the gross domestic product (GDP), energy consumption, and electric generation data of 30 provinces from 2001 to 2015, a hybrid trend forecasting model, and a three-indicator allocation model to measure the provincial quota allocation for carbon emissions in China′s electric power sector. The conclusions drawn from the empirical analysis can be summarized as follows: (1) The carbon emission peak in China′s electric power sector will appear in 2027, and peak emissions will be 3.63 billion tons, which will surpass the total carbon emissions of the European Union (EU) and approximately equal to 2/3 of the United States of America (USA). (2) The developed provinces that are supported by traditional industries should take more responsibility for carbon mitigation. (3) Nine provinces are expected to be the buyers in the CET market. These provinces are mostly located in eastern China, and account for approximately 63.65% of China′s carbon emissions generated by the electric power sector. (4) The long-distance electric power transmission shifts the carbon emissions and then has an impact on the quotas allocation for carbon emissions. (5) The development and effective utilization of clean power generation will play a positive role for carbon mitigation in China′s electric sector.
Ming Meng; Lixue Wang; Qu Chen. Quota Allocation for Carbon Emissions in China′s Electric Power Industry Based Upon the Fairness Principle. Energies 2018, 11, 2256 .
AMA StyleMing Meng, Lixue Wang, Qu Chen. Quota Allocation for Carbon Emissions in China′s Electric Power Industry Based Upon the Fairness Principle. Energies. 2018; 11 (9):2256.
Chicago/Turabian StyleMing Meng; Lixue Wang; Qu Chen. 2018. "Quota Allocation for Carbon Emissions in China′s Electric Power Industry Based Upon the Fairness Principle." Energies 11, no. 9: 2256.
To guide the policy adjustment in the new normal economic mode, this study uses a range-adjusted measure and data envelopment analysis model to evaluate the provincial comprehensive efficiencies and explore the factors causing poor performance in China during 2001–2014. Empirical analysis draws the following conclusions. 1) The comprehensive efficiencies of most provinces present upward trends, which imply the positively contribution of previous efficiency-related policies. 2) Contrary to economic levels, the comprehensive efficiency of the central region is lower than that in the east but higher than that in the west. This result confirms the existence of extensive growth and implies that developed regions should be largely responsible for the low comprehensive efficiency of the country. 3) Except for a few outliers, the comprehensive efficiency level of a province is highly correlated to its climatic characteristics. This phenomenon offers new ideas for the central government to create differential efficiency-related policies. 4) For east provinces, labor input, energy input, gross domestic product (GDP) output, and CO2 emissions are all important in improving the comprehensive efficiency. Efficiency-related policies should focus on technical progress. For west and central provinces, labor input and GDP output are the main contributors to inefficiency. The key policy direction of these provinces should be the improvement of human resource efficiency.
Ming Meng; Yanan Fu; Lixue Wang. Low-carbon economy efficiency analysis of China's provinces based on a range-adjusted measure and data envelopment analysis model. Journal of Cleaner Production 2018, 199, 643 -650.
AMA StyleMing Meng, Yanan Fu, Lixue Wang. Low-carbon economy efficiency analysis of China's provinces based on a range-adjusted measure and data envelopment analysis model. Journal of Cleaner Production. 2018; 199 ():643-650.
Chicago/Turabian StyleMing Meng; Yanan Fu; Lixue Wang. 2018. "Low-carbon economy efficiency analysis of China's provinces based on a range-adjusted measure and data envelopment analysis model." Journal of Cleaner Production 199, no. : 643-650.
Ming Meng; Yanan Fu; Xinfang Wang. Decoupling, decomposition and forecasting analysis of China's fossil energy consumption from industrial output. Journal of Cleaner Production 2018, 177, 752 -759.
AMA StyleMing Meng, Yanan Fu, Xinfang Wang. Decoupling, decomposition and forecasting analysis of China's fossil energy consumption from industrial output. Journal of Cleaner Production. 2018; 177 ():752-759.
Chicago/Turabian StyleMing Meng; Yanan Fu; Xinfang Wang. 2018. "Decoupling, decomposition and forecasting analysis of China's fossil energy consumption from industrial output." Journal of Cleaner Production 177, no. : 752-759.
Energy efficiency improvement is essential for China’s sustainable development of its social economy. Based on the provincial panel data of China’s three economic regions from 1990 to 2013, this research uses the data envelopment analysis (DEA) model to measure the total-factor energy efficiency, and the Tobit regression model to explore the driving factors of efficiency changes. Empirical results show: (1) Energy efficiency, energy consumption structure, and government fiscal scale are significantly positively correlated. (2) Industrial structure and per capita income level have negative correlation to energy efficiency; the impact of industrial structure on energy efficiency is relatively small. (3) The increase of carbon dioxide emissions will decrease the energy efficiency. Furthermore, with people becoming less conscious of energy conservation and emission reduction, energy efficiency will also decrease. (4) Specific energy policies will improve energy efficiency, and greater openness in coastal areas will also have the similar effect.
Sheng-An Shi; Long Xia; Ming Meng. Energy Efficiency and Its Driving Factors in China’s Three Economic Regions. Sustainability 2017, 9, 2059 .
AMA StyleSheng-An Shi, Long Xia, Ming Meng. Energy Efficiency and Its Driving Factors in China’s Three Economic Regions. Sustainability. 2017; 9 (11):2059.
Chicago/Turabian StyleSheng-An Shi; Long Xia; Ming Meng. 2017. "Energy Efficiency and Its Driving Factors in China’s Three Economic Regions." Sustainability 9, no. 11: 2059.
Annual electricity consumption forecasting is one of the important foundations of power system planning. Considering that the long-term electricity consumption curves of developing countries usually present approximately exponential growth trends and linear and accelerated growth rate trends may also appear in certain periods, this paper first proposes a small-sample adaptive hybrid model (AHM) to extrapolate the above curves. The iterative trend extrapolation equation of the proposed model can simulate the linear, exponential, and steep trends adaptively at the same time. To estimate the equation parameters using small samples, the partial least squares (PLS) and iteration starting point optimization algorithms are suggested. To evaluate forecasting performance, the artificial neural network (ANN), grey model (GM), and AHM are used to forecast electricity consumption in China from 1991 to 2014, and then the results of these models are compared. Analysis of the forecasting results shows that the AHM can overcome stochastic changes and respond quickly to changes in the main electricity consumption trend because of its specialized equation structure. Overall error analysis indicators also show that AHM often obtains more precise forecasting results than the other two models.
Ming Meng; Yanan Fu; Huifeng Shi; Xinfang Wang. A Small-Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting. Mathematical Problems in Engineering 2017, 2017, 1 -7.
AMA StyleMing Meng, Yanan Fu, Huifeng Shi, Xinfang Wang. A Small-Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting. Mathematical Problems in Engineering. 2017; 2017 ():1-7.
Chicago/Turabian StyleMing Meng; Yanan Fu; Huifeng Shi; Xinfang Wang. 2017. "A Small-Sample Adaptive Hybrid Model for Annual Electricity Consumption Forecasting." Mathematical Problems in Engineering 2017, no. : 1-7.
Industrial energy and environment efficiency evaluation become especially crucial as industrial sectors play a key role in CO2 emission reduction and energy consumption. This study adopts the additive range-adjusted measure data envelope analysis (RAM-DEA) model to estimate the low-carbon economy efficiency of Chinese industrial sectors in 2001–2013. In addition, the CO2 emission intensity mitigation target for each industrial sector is assigned. Results show that, first, most sectors are not completely efficient, but they have experienced and have improved greatly during the period. These sectors can be divided into four categories, namely, mining, light, heavy, and electricity, gas, and water supply industries. The efficiency is diverse among the four industrial categories. The average efficiency of the light industry is the highest among the industries, followed by those of the mining and the electricity, gas, and water supply industries, and that of the heavy industry is the lowest. Second, the electricity, gas, and water supply industry shows the biggest potential for CO2 emission reduction, thus containing most of the sectors with large CO2 emission intensity mitigation targets (more than 45%), followed by the mining and the light industries. Therefore, the Chinese government should formulate diverse and flexible policy implementations according to the actual situation of the different sectors. Specifically, the sectors with low efficiency should be provided with additional policy support (such as technology and finance aids) to improve their industrial efficiency, whereas the electricity, gas, and water supply industry should maximize CO2 emission reduction.
Ming Meng; Yanan Fu; Tianyu Wang; Kaiqiang Jing. Analysis of Low-Carbon Economy Efficiency of Chinese Industrial Sectors Based on a RAM Model with Undesirable Outputs. Sustainability 2017, 9, 451 .
AMA StyleMing Meng, Yanan Fu, Tianyu Wang, Kaiqiang Jing. Analysis of Low-Carbon Economy Efficiency of Chinese Industrial Sectors Based on a RAM Model with Undesirable Outputs. Sustainability. 2017; 9 (3):451.
Chicago/Turabian StyleMing Meng; Yanan Fu; Tianyu Wang; Kaiqiang Jing. 2017. "Analysis of Low-Carbon Economy Efficiency of Chinese Industrial Sectors Based on a RAM Model with Undesirable Outputs." Sustainability 9, no. 3: 451.
To guide the adjustment of the electricity policies of the 13th Five-year Plan and the present market-oriented reforms, this paper performs a scenario analysis of the CO2 emissions from China's electric power industry. By using a logarithmic linear equation to explain the relationship between CO2 emissions and their influence factors, a hybrid model to forecast the “natural” development of the explanatory variables, and emission-relative data from 2001 to 2013 to estimate the equation parameters, five scenarios are designed, and the CO2 emissions of each scenario are forecasted for the period of 2016–2030. On the basis of the modeling results, the sensitivity and contribution of each variable are also measured. Empirical analysis draws the following conclusions. (1) The electric power industry of China cannot easily reach its CO2 emission peak before 2030. This will impose considerable pressure on the Chinese government to realize its emission mitigation target. (2) Compared with the non-fossil energy share in electricity generation, the CO2 emissions from China's electric power industry are more sensitive to the changes in total electricity consumption and thermal power generation efficiency. (3) The increase in total electricity consumption is the single most important contributor to CO2 emission growth from the electric power industry of China. (4) To mitigate future CO2 emissions from the electric power industry, the Chinese government should optimize the industrial export structure and enhance its awareness of the increase in household electricity consumption.
Ming Meng; Kaiqiang Jing; Sarah Mander. Scenario analysis of CO2 emissions from China's electric power industry. Journal of Cleaner Production 2016, 142, 3101 -3108.
AMA StyleMing Meng, Kaiqiang Jing, Sarah Mander. Scenario analysis of CO2 emissions from China's electric power industry. Journal of Cleaner Production. 2016; 142 ():3101-3108.
Chicago/Turabian StyleMing Meng; Kaiqiang Jing; Sarah Mander. 2016. "Scenario analysis of CO2 emissions from China's electric power industry." Journal of Cleaner Production 142, no. : 3101-3108.
The extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model has been applied to analyzing the relationship between CO2 emissions from power industry and the influential factors for the period from 1997 to 2020. The two groups found through partial least square (PLS) regularity test show two important areas for CO2 emissions reduction from the power industry: economic activity and low-carbon electric technology. Moreover, considering seven influential factors (economic activity, population, urbanization level, industrial structure, electricity intensity, generation structure, and energy intensity) that affect the power CO2 emissions and the practical situation in the power sector, possible development scenarios for the 13th Five-Year Plan period were designed, and the corresponding CO2 emissions from the power sector for different scenarios were estimated. Through scenario analysis, the potential mitigation of emissions from power industry can be determined. Moreover, the CO2 emissions reduction rates in the different scenarios indicate the possible low-carbon development directions and policies for the power industry during the period of the 13th Five Year Plan.
Wei Sun; Ming Meng; Yujun He; Hong Chang. CO2 Emissions from China’s Power Industry: Scenarios and Policies for 13th Five-Year Plan. Energies 2016, 9, 825 .
AMA StyleWei Sun, Ming Meng, Yujun He, Hong Chang. CO2 Emissions from China’s Power Industry: Scenarios and Policies for 13th Five-Year Plan. Energies. 2016; 9 (10):825.
Chicago/Turabian StyleWei Sun; Ming Meng; Yujun He; Hong Chang. 2016. "CO2 Emissions from China’s Power Industry: Scenarios and Policies for 13th Five-Year Plan." Energies 9, no. 10: 825.
This paper provides a quantitative analysis of the sensitivity, amount, and the development trend of carbon emissions embodied in China's international trade. With the input-output technique, nonhomogeneous exponential growth model, and carbon transmission-relative data, the following conclusions were drawn: (a) The total (direct and indirect) carbon intensity of each industrial sector was measured. Of all the 27 industrial sectors, Production and Supply of Electric Power and Heat Power ranks first. Because of the large consumption of electric power by nearly all the industrial sectors, encouraging the electric power sectors to utilize non-fossil energy (especially wind and photovoltaics), to improve the generation efficiency, and to import electric power overseas is crucial for decreasing the overall level of China's carbon intensity. (b) The amount of carbon transmission embodied in exports and imports of each industrial sector was also measured. Owing to its enormous international trade values, the sector of Manufacture of Electrical Machinery and Equipment ranks first, with absolute predominance in both exports and imports. Adjusting China's industrial policy to decrease the net export of this sector would significantly reduce the amount of net carbon transmission in the country. (c) The future net carbon transmission of each industrial sector was forecasted. Trend analysis indicates that changes in the overall international trade situation would cause the carbon transmission amount embodied in exports in China to become less than that embodied in imports since 2015.
Wei Shang; Guifen Pei; Ming Meng; Dongxiao Niu. Sensitivity and trend analysis of carbon emissions embodied in China's international trade with input-output technique and nonhomogeneous exponential growth model. Journal of Renewable and Sustainable Energy 2016, 8, 055902 .
AMA StyleWei Shang, Guifen Pei, Ming Meng, Dongxiao Niu. Sensitivity and trend analysis of carbon emissions embodied in China's international trade with input-output technique and nonhomogeneous exponential growth model. Journal of Renewable and Sustainable Energy. 2016; 8 (5):055902.
Chicago/Turabian StyleWei Shang; Guifen Pei; Ming Meng; Dongxiao Niu. 2016. "Sensitivity and trend analysis of carbon emissions embodied in China's international trade with input-output technique and nonhomogeneous exponential growth model." Journal of Renewable and Sustainable Energy 8, no. 5: 055902.
Achieving the decoupling of electric CO2 emissions from total electricity generation is important in ensuring the sustainable socioeconomic development of China. To realize this, China implemented market-oriented reforms to its electric power industry at the beginning of 2003. This study used the Tapio decoupling index, the Laspeyres decomposition algorithm, and decoupling-related data from 1993 to 2012 to evaluate the effect of these reforms. Several conclusions can be drawn based on the empirical analysis. (1) The reforms changed the developmental trend of the decoupling index and facilitated its progress towards strong decoupling. (2) The results forecasted through fitting the curve to the decoupling index indicate that strong decoupling would be realized by 2030. (3) Limiting the manufacturing development and upgrading the generation equipment of the thermal power plants are essential for China to achieve strong decoupling at an early date. (4) China should enhance regulatory pressures and guidance for appropriate investment in thermal power plants to ensure the stable development of the decoupling index. (5) Transactions between multiple participants and electricity price bidding play active roles in the stable development of the decoupling index.
Wei Shang; Guifen Pei; Conor Walsh; Ming Meng; Xiangsong Meng. Have Market-oriented Reforms Decoupled China’s CO2 Emissions from Total Electricity Generation? An Empirical Analysis. Sustainability 2016, 8, 468 .
AMA StyleWei Shang, Guifen Pei, Conor Walsh, Ming Meng, Xiangsong Meng. Have Market-oriented Reforms Decoupled China’s CO2 Emissions from Total Electricity Generation? An Empirical Analysis. Sustainability. 2016; 8 (5):468.
Chicago/Turabian StyleWei Shang; Guifen Pei; Conor Walsh; Ming Meng; Xiangsong Meng. 2016. "Have Market-oriented Reforms Decoupled China’s CO2 Emissions from Total Electricity Generation? An Empirical Analysis." Sustainability 8, no. 5: 468.
Based on the international community’s analysis of the present CO2 emissions situation, a Log Mean Divisia Index (LMDI) decomposition model is proposed in this paper, aiming to reflect the decomposition of carbon productivity. The model is designed by analyzing the factors that affect carbon productivity. China’s contribution to carbon productivity is analyzed from the dimensions of influencing factors, regional structure and industrial structure. It comes to the conclusions that: (a) economic output, the provincial carbon productivity and energy structure are the most influential factors, which are consistent with China’s current actual policy; (b) the distribution patterns of economic output, carbon productivity and energy structure in different regions have nothing to do with the Chinese traditional sense of the regional economic development patterns; (c) considering the regional protectionism, regional actual situation need to be considered at the same time; (d) in the study of the industrial structure, the contribution value of industry is the most prominent factor for China’s carbon productivity, while the industrial restructuring has not been done well enough.
Jianchang Lu; Weiguo Fan; Ming Meng. Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors. Energies 2015, 8, 3093 -3117.
AMA StyleJianchang Lu, Weiguo Fan, Ming Meng. Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors. Energies. 2015; 8 (4):3093-3117.
Chicago/Turabian StyleJianchang Lu; Weiguo Fan; Ming Meng. 2015. "Empirical Research on China’s Carbon Productivity Decomposition Model Based on Multi-Dimensional Factors." Energies 8, no. 4: 3093-3117.
This study investigates the dynamic relationship between economic growth and CO
Qian Gao; Ming Meng; Lei Wen; Dongxiao Niu. Analysing the environmental Kuznets curve for CO2 emissions in China using segmented equations and partial least squares. International Journal of Global Warming 2015, 7, 518 .
AMA StyleQian Gao, Ming Meng, Lei Wen, Dongxiao Niu. Analysing the environmental Kuznets curve for CO2 emissions in China using segmented equations and partial least squares. International Journal of Global Warming. 2015; 7 (4):518.
Chicago/Turabian StyleQian Gao; Ming Meng; Lei Wen; Dongxiao Niu. 2015. "Analysing the environmental Kuznets curve for CO2 emissions in China using segmented equations and partial least squares." International Journal of Global Warming 7, no. 4: 518.
This paper presents the first attempt to analyse the driving force factors of global carbon intensity changes. The said changes from 1998 to 2009 are decomposed into a summation of the quantitative effects of three dimensions: 1) time; 2) emitter (195 countries and regions); 3) influence factor (technological innovation and economic structural adjustment). The decomposition results revealed several important conclusions. First, although both countries are super emitters, the USA and China greatly differ in decreasing global intensity, with the former being the most important contributor and the latter being the most important deterrent. Second, the rate of change in an emitter's carbon intensity is considered an indicator of progress speed in realising sustainable development. This change is not related to the emitter's level of economic development. Emitters with similar rates of change tend to cluster together according to their geographic locations. Finally, the annual change in global carbon intensity that is influenced by technological innovation is irregular, whereas those caused by economic structural adjustment present a linear increasing trend, which imposes increasing pressure on the need to decrease global carbon intensity.
Ming Meng; Dongxiao Niu; Jinpeng Liu; Xiaomin Xu. Driving force factor analysis of global carbon intensity changes. International Journal of Global Warming 2015, 7, 78 .
AMA StyleMing Meng, Dongxiao Niu, Jinpeng Liu, Xiaomin Xu. Driving force factor analysis of global carbon intensity changes. International Journal of Global Warming. 2015; 7 (1):78.
Chicago/Turabian StyleMing Meng; Dongxiao Niu; Jinpeng Liu; Xiaomin Xu. 2015. "Driving force factor analysis of global carbon intensity changes." International Journal of Global Warming 7, no. 1: 78.