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Remanufacture unlocks the residual values in used products by prolonging the life cycle. While due to different working conditions in use stage, the quality of returned items varies widely, which has greatly reduced the process efficiency and added to the complexity of management. To quantify the impacts of quality uncertainty on remanufacture, the study employs Monte Carlo simulation to establish definite relation between quality loss and environmental efficiency in remanufacturing, then from which conducts a quantitative analysis of the effects of carbon trading mechanism on procurement and production. Finally, it devises a multi-objective optimization strategy to strike a balance between economic and environmental benefits. Grounded with data collected from the machine remanufacturing industry, our model illustrates that moderately raising the environmental regulation stringency will not impede the industrial development, it could increase the comprehensive benefit by even up to 50%. Thus, we provide a theoretical foundation and optimization direction for managers to make scientific decision towards sustainable development at lower energy and resource input in remanufacturing.
Haolan Liao; Changxu Li; Yongyou Nie; Jing Tan; Kui Liu. Environmental efficiency assessment for remanufacture of end of life machine and multi-objective optimization under carbon trading mechanism. Journal of Cleaner Production 2021, 308, 127168 .
AMA StyleHaolan Liao, Changxu Li, Yongyou Nie, Jing Tan, Kui Liu. Environmental efficiency assessment for remanufacture of end of life machine and multi-objective optimization under carbon trading mechanism. Journal of Cleaner Production. 2021; 308 ():127168.
Chicago/Turabian StyleHaolan Liao; Changxu Li; Yongyou Nie; Jing Tan; Kui Liu. 2021. "Environmental efficiency assessment for remanufacture of end of life machine and multi-objective optimization under carbon trading mechanism." Journal of Cleaner Production 308, no. : 127168.
In order to improve China's environmental quality, the central government has strengthened the environmental responsibility of local governments, and adopted supervision tools such as environmental protection inquiries and inspections to promote local governments to be more proactive in environmental issues. In this paper, the global non-radial direction distance function (NDDF) and difference-in-difference (DID) method are used to analyze the impact of the environmental protection inquiry on energy-environmental efficiency. Specifically, taking energy, capital, and labor as input factors, regional GDP as desirable output, and sulfur dioxide, dust, and wastewater emissions as undesirable outputs, the unified energy-environmental efficiency index (UEEI) of each city has been estimated with the global NDDF method. The results show that UEEI in the eastern and central regions are relatively high, while that in the north and northeast regions are relatively low. On this basis, the impact of environmental protection inquiry on UEEI of each city is analyzed with the DID method. The results show that the environmental protection inquiry can significantly improve the energy-environmental efficiency of atmospheric pollutants, but the effect on energy-environmental efficiency of wastewater emission is not significant.
Yongyou Nie; Dandan Cheng; Kui Liu. The effectiveness of environmental authoritarianism: Evidence from China's administrative inquiry for environmental protection. Energy Economics 2020, 88, 104777 .
AMA StyleYongyou Nie, Dandan Cheng, Kui Liu. The effectiveness of environmental authoritarianism: Evidence from China's administrative inquiry for environmental protection. Energy Economics. 2020; 88 ():104777.
Chicago/Turabian StyleYongyou Nie; Dandan Cheng; Kui Liu. 2020. "The effectiveness of environmental authoritarianism: Evidence from China's administrative inquiry for environmental protection." Energy Economics 88, no. : 104777.
One-child policy has been accused of causing high saving rates in China, and this article provides direct evidences for the economic impact of the two-child policy on savings. Using China Migrants Dynamic Survey, we find the two-child policy significantly reduces the household saving rates by 1.96 percentage points in average and by 4.88 percentage points if the rural and ethnic minority groups excluded, and we also use IV and propensity score matching (PSM) estimation methods to test the robustness. Furthermore, the study shows that the two-child policy has a heterogeneous effect on the saving rates decreasing, which mainly occurs in the local hukou population group. From this perspective, the policy has a comprehensive and sustainable impact. Through mechanism analysis, we clarify that the two-child policy affects savings by increasing expenditure insignificantly; in addition to the direct effect, it mainly affects savings by transiently reducing family income. Abbreviations: IV: Instrumental variables; PSM: Propensity score matching; CPI: Consumer price index; OLS: Ordinary least squares
Jing Tan; Kui Liu. The impact of two-child policy on household savings in China and mechanisms. Applied Economics Letters 2020, 27, 1672 -1676.
AMA StyleJing Tan, Kui Liu. The impact of two-child policy on household savings in China and mechanisms. Applied Economics Letters. 2020; 27 (20):1672-1676.
Chicago/Turabian StyleJing Tan; Kui Liu. 2020. "The impact of two-child policy on household savings in China and mechanisms." Applied Economics Letters 27, no. 20: 1672-1676.
In current work, the phenomenon of NIMBY (not in my back yard) for a municipal solid waste incinerator was recognized through an investigation for the evolution of individual risk attitude to group risk attitude (ItGRA). The cellular automaton model was employed to evaluate the risk attitude status with different frequencies of social interaction between residents. In the simulation case, the risk attitude of residents in the pseudo-rational state and non-pseudo-rational state was evaluated, which indicates the sheep-flock effect on the exaggeration of public NIMBY attitude. To the incinerator, the individual risk attitude evolved to supportive group risk attitude at a social interaction frequency 100 times higher than that in family or local neighborhoods, when the initial number of residents in opposition and support was equal. This was supported by the result of the model in the evaluation of resident risk attitude around the incinerator in Shanghai. On the contrary, for those in a non-pseudo-rational state, the ultimate group risk attitude depends on the probability that the residents have a supportive or opposing risk attitude as the concept of individuals was difficult to change. Accordingly, the decision strategy of incinerator construction should consider the influence of the sheep-flock effect, which can increase the attitude of residents in support and lead to the evolution of a group risk attitude to support attitude. Therefore, this study provides insight into the evolution of public attitude to NIMBY attitude and a promising evaluation method to quantify and guide the individual and group risk attitudes.
Jinbu Zhao; Yongyou Nie; Kui Liu; Jizhi Zhou. Evolution of the Individual Attitude in the Risk Decision of Waste Incinerator Construction: Cellular Automaton Model. Sustainability 2020, 12, 368 .
AMA StyleJinbu Zhao, Yongyou Nie, Kui Liu, Jizhi Zhou. Evolution of the Individual Attitude in the Risk Decision of Waste Incinerator Construction: Cellular Automaton Model. Sustainability. 2020; 12 (1):368.
Chicago/Turabian StyleJinbu Zhao; Yongyou Nie; Kui Liu; Jizhi Zhou. 2020. "Evolution of the Individual Attitude in the Risk Decision of Waste Incinerator Construction: Cellular Automaton Model." Sustainability 12, no. 1: 368.
As the world's most energy-consuming and carbon-emitting country, China faces enormous pressures on energy conservation and emission reduction, and improving energy efficiency is one of the most important ways to save energy and reduce emissions. Using the city-level panel data in China during 2013–2017, we apply the global non-radial directional distance function (NDDF) to estimate the global unified efficiency (GUE) of each city as well as their driving forces, and identify the change of efficiency performance. The results indicate that the average GUE changed −1.0%, 1.2%, 6.0% and 7.0% during 2013–2014, 2014–2015, 2015–2016 and 2016–2017, respectively. The more developed Central China and the relatively underdeveloped Northwest China have high GUE, while the lower GUE exists in the Northeast and North China regions with greater industrial transformation and upgrading pressures. In general, the global unified efficiency of each region increases over time.
Kui Liu; Suying Lu; Guanglu Zhang. Regional difference in global unified efficiency of China—Evidence from city-level data. Science of The Total Environment 2019, 713, 136355 .
AMA StyleKui Liu, Suying Lu, Guanglu Zhang. Regional difference in global unified efficiency of China—Evidence from city-level data. Science of The Total Environment. 2019; 713 ():136355.
Chicago/Turabian StyleKui Liu; Suying Lu; Guanglu Zhang. 2019. "Regional difference in global unified efficiency of China—Evidence from city-level data." Science of The Total Environment 713, no. : 136355.
Greenhouse gas (GHG) emissions are an important factor in the evaluation of green industrial growth, when low GHG emissions along with high industrial growth are expected. In this paper, the improvement of sustainable development of industry in China (2007–2015) was investigated via analysis of the relationships between the GHG emissions and energy consumption in comparison to European countries. A hierarchical cluster analysis (HCA) was conducted to distinguish industrial growth with GHG emission and energy consumption structures. The results of this research indicated that green industrial growth in Europe had a negative annual rate of GHG emissions. This contributed to the ratio of renewable energy consumption increasing to a maximum of 33% and an average of 16%. In comparison, the GHG emissions in China increased at a rate of 50% to 77% in the main industrial provinces since 2007 with their rapid industrial growth. The rate of GHG emissions decreased after 2012, which was 7% or less than the rate of emissions in the industrial provinces. Contrary to in Europe, the decreasing rate of GHG emissions in China was attributed to the improvement of fossil energy efficiency, as renewable energy consumption was less than 10% in most industrial provinces. Our data analysis identified that the two different energy consumption strategies improved green industrial growth in Europe and China, respectively. Our data analysis identified the two different energy consumption strategies employed by Europe and China, each of which promoted green industrial growth in the corresponding areas. We concluded that China achieved green industrial growth through an increase in energy efficiency through technology updates to decrease GHG emissions, which we call the “China Model.” The “Europe Model” proved to be quite different, having the core characteristic of increasing renewable energy use.
Yanbing Mao; Kui Liu; Jizhi Zhou. Evolution of Green Industrial Growth between Europe and China based on the Energy Consumption Model. Sustainability 2019, 11, 7168 .
AMA StyleYanbing Mao, Kui Liu, Jizhi Zhou. Evolution of Green Industrial Growth between Europe and China based on the Energy Consumption Model. Sustainability. 2019; 11 (24):7168.
Chicago/Turabian StyleYanbing Mao; Kui Liu; Jizhi Zhou. 2019. "Evolution of Green Industrial Growth between Europe and China based on the Energy Consumption Model." Sustainability 11, no. 24: 7168.
As the problem of China’s environmental pollution is becoming more serious, people have paid more attention to the environmental pollution control. In this paper, we first built a comprehensive environmental pollution index including sulfur dioxide, smoke and dust, wastewater and solid waste emissions to measure the environmental pollution of each province synthetically. The method of space econometrics was then applied to analyze the main influencing factors of environmental pollution. Three conclusions were drawn from the empirical results. First, there is a significant spatial correlation between environmental pollution of different provinces. Second, there exists an inverted N relationship between environmental pollution and the economic development, moreover, most provinces have not arrived the second turning point of the inverted N curve except for several economically developed provinces. Third, both industrial structure and R&D investment have a significant influence on environmental pollution, while the influence of FDI is not significant. All these imply the necessity to establish a regional joint prevention and control mechanism to cope with the increasing environmental pollution.
Kui Liu; Boqiang Lin. Research on influencing factors of environmental pollution in China: A spatial econometric analysis. Journal of Cleaner Production 2018, 206, 356 -364.
AMA StyleKui Liu, Boqiang Lin. Research on influencing factors of environmental pollution in China: A spatial econometric analysis. Journal of Cleaner Production. 2018; 206 ():356-364.
Chicago/Turabian StyleKui Liu; Boqiang Lin. 2018. "Research on influencing factors of environmental pollution in China: A spatial econometric analysis." Journal of Cleaner Production 206, no. : 356-364.
In this paper, we investigate how road infrastructure affects the energy consumption and development of energy intensive industries in China. From the perspective of profit function, the endogeneity problem which may be caused by reverse causality can be avoided. China’s provincial data for the period 2000-2013 and seemingly uncorrelated regression method are used to estimate the parameters. The results show that increase in road density will increase energy consumption and promote the development of energy intensive industries. The short term elasticity of output with respect to road infrastructure is smaller than the long run elasticity. However, this is not the case for energy consumption. Additionally, increase in road density can reduce energy intensity only in the long run and the decrease is largest in western China. Therefore, more road construction in the western regions will be more helpful for energy intensive industries in their bid to reduce energy intensity. Furthermore, we find evidence of energy price distortion in China’s energy intensive industries. Lastly, we calculate the changes in output and energy consumption of China’s energy intensive industries both in the short and long run caused by the increase in road density from 2000 to 2013.
Ruipeng Tan; Kui Liu; Boqiang Lin. Transportation infrastructure development and China’s energy intensive industries - A road development perspective. Energy 2018, 149, 587 -596.
AMA StyleRuipeng Tan, Kui Liu, Boqiang Lin. Transportation infrastructure development and China’s energy intensive industries - A road development perspective. Energy. 2018; 149 ():587-596.
Chicago/Turabian StyleRuipeng Tan; Kui Liu; Boqiang Lin. 2018. "Transportation infrastructure development and China’s energy intensive industries - A road development perspective." Energy 149, no. : 587-596.
With rapid development and large scale urbanization, China's environmental and resource constraints are becoming increasingly severe. Compared to other industries, China's heavy industry is energy-intensive and emission-intensive, which exerts pressure on energy conservation policies. In this paper, by establishing a theoretical model of the impact factors of energy intensity, we investigate the effects of energy prices, ownership structure, and industrial concentration and R&D investment on the energy intensity of China's heavy industry, and seemingly unrelated regression model was used in the estimation of corresponding coefficients. The results show that increase in energy prices, decrease in state-owned enterprises and increase in industrial concentration may reduce the energy intensity of the industry. Also, the study points to evidence that an increase in R&D investment may reduce the oil intensity of heavy industry.
Kui Liu; Hongkun Bai; Jiangbo Wang; Boqiang Lin. How to reduce energy intensity in China's heavy industry—Evidence from a seemingly uncorrelated regression. Journal of Cleaner Production 2018, 180, 708 -715.
AMA StyleKui Liu, Hongkun Bai, Jiangbo Wang, Boqiang Lin. How to reduce energy intensity in China's heavy industry—Evidence from a seemingly uncorrelated regression. Journal of Cleaner Production. 2018; 180 ():708-715.
Chicago/Turabian StyleKui Liu; Hongkun Bai; Jiangbo Wang; Boqiang Lin. 2018. "How to reduce energy intensity in China's heavy industry—Evidence from a seemingly uncorrelated regression." Journal of Cleaner Production 180, no. : 708-715.
Kui Liu; Hongkun Bai; Shuo Yin; Boqiang Lin. Factor substitution and decomposition of carbon intensity in China's heavy industry. Energy 2018, 145, 582 -591.
AMA StyleKui Liu, Hongkun Bai, Shuo Yin, Boqiang Lin. Factor substitution and decomposition of carbon intensity in China's heavy industry. Energy. 2018; 145 ():582-591.
Chicago/Turabian StyleKui Liu; Hongkun Bai; Shuo Yin; Boqiang Lin. 2018. "Factor substitution and decomposition of carbon intensity in China's heavy industry." Energy 145, no. : 582-591.
A translog production function model with input factors including energy, capital, and labor is established for China’s heavy industry. Using the ridge regression method, the output elasticity of each input factor and the substitution elasticity between input factors are analyzed. The empirical results show that the output elasticity of energy, capital and labor are all positive, while the output elasticities of energy and capital are relatively higher, indicating that China’s heavy industry is energy- and capital-intensive. Simultaneously, all the input factors are substitutes, with the substitution between labor and energy having the highest degree of responsiveness. The substitution elasticity between labor and energy is decreasing, while the substitution elasticities of capital for energy and labor are increasing. More capital input can help to improve energy efficiency and thus accomplish the goal of energy conservation in China’s heavy industry.
Boqiang Lin; Kui Liu. Energy Substitution Effect on China’s Heavy Industry: Perspectives of a Translog Production Function and Ridge Regression. Sustainability 2017, 9, 1892 .
AMA StyleBoqiang Lin, Kui Liu. Energy Substitution Effect on China’s Heavy Industry: Perspectives of a Translog Production Function and Ridge Regression. Sustainability. 2017; 9 (11):1892.
Chicago/Turabian StyleBoqiang Lin; Kui Liu. 2017. "Energy Substitution Effect on China’s Heavy Industry: Perspectives of a Translog Production Function and Ridge Regression." Sustainability 9, no. 11: 1892.
China is facing huge pressure on CO2 emissions reduction. The heavy industry accounts for over 60% of China’s total energy consumption, and thus leads to a large number of energy-related carbon emissions. This paper adopts the Log Mean Divisia Index (LMDI) method based on the extended Kaya identity to explore the influencing factors of CO2 emissions from China’s heavy industry; we calculate the trend of decoupling by presenting a theoretical framework for decoupling. The results show that labor productivity, energy intensity, and industry scale are the main factors affecting CO2 emissions in the heavy industry. The improvement of labor productivity is the main cause of the increase in CO2 emissions, while the decline in energy intensity leads to CO2 emissions reduction, and the industry scale has different effects in different periods. Results from the decoupling analysis show that efforts made on carbon emission reduction, to a certain extent, achieved the desired outcome but still need to be strengthened.
Lin Boqiang; Kui Liu. Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry. Sustainability 2017, 9, 1198 .
AMA StyleLin Boqiang, Kui Liu. Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry. Sustainability. 2017; 9 (7):1198.
Chicago/Turabian StyleLin Boqiang; Kui Liu. 2017. "Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry." Sustainability 9, no. 7: 1198.
Heavy industry accounts for nearly 65% of the energy consumption and over 60% of the electricity consumption of China. Under the framework of real savings and green GDP, the huge energy consumption and carbon emissions will bring in huge natural resource losses, and then affect the total factor productivity (TFP) seriously. When taking the input–output relationship into consideration, the natural resource losses of heavy industry will decrease significantly. As the upstream of the industrial chain, heavy industry offered a large number of subsidies to the downstream industries by providing energy, raw materials, and taking on carbon emissions. This article verified the transfer of natural resource losses among industries, and estimated the real TFP of heavy industry from input–output and traditional perspective, respectively. The results showed that there was an increasing trend in the growth rate of heavy industry’s TFP in the perspective of input–output.
Boqiang Lin; Kui Liu. How Efficient Is China’s Heavy Industry? A Perspective of Input–Output Analysis. Emerging Markets Finance and Trade 2015, 52, 2546 -2564.
AMA StyleBoqiang Lin, Kui Liu. How Efficient Is China’s Heavy Industry? A Perspective of Input–Output Analysis. Emerging Markets Finance and Trade. 2015; 52 (11):2546-2564.
Chicago/Turabian StyleBoqiang Lin; Kui Liu. 2015. "How Efficient Is China’s Heavy Industry? A Perspective of Input–Output Analysis." Emerging Markets Finance and Trade 52, no. 11: 2546-2564.