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Dandan Wang
School of Economics and Management, North China Electric Power University, Beijing, China

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Research article
Published: 10 August 2021 in Environmental Science and Pollution Research
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This study attempts to analyze the impact of population, property, technology, energy factors, and spatial agglomeration in the logistics industry on carbon emissions. To achieve the goal of peak carbon and carbon neutrality, the relationship between influencing factors and carbon emissions was analyzed based on panel data from the logistics industry for 30 provinces in China from 2003 to 2017 using an improved STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model and a spatial lag model (SLM). The results show that population, property, technology, and energy factors in the logistics industry all have different degrees of influence on carbon emissions, wherein population, energy, and property have a greater influence, which implies that carbon emission reduction policies can be carried out considering the relevant aspects. In addition, under the influence of spatial agglomeration, the degree of influence of freight mileage (FM), total fixed-asset investment (TFAI), and industry population (IPOP) on carbon emissions decreases, and the degree of influence of energy intensity (EI) and industry per capita GDP (IPCG) increases. This suggests that corresponding emission reduction policies should be formulated for large urban areas based on technological innovation, infrastructure, and talent training, while smaller urban areas can focus on developing new energy and industrial economies. These findings help to complement the existing literature and provide policymakers with some insights related to urban logistics development.

ACS Style

Xiaopeng Guo; Dandan Wang. Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China. Environmental Science and Pollution Research 2021, 1 -13.

AMA Style

Xiaopeng Guo, Dandan Wang. Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China. Environmental Science and Pollution Research. 2021; ():1-13.

Chicago/Turabian Style

Xiaopeng Guo; Dandan Wang. 2021. "Analysis of the spatial relevance and influencing factors of carbon emissions in the logistics industry from China." Environmental Science and Pollution Research , no. : 1-13.

Journal article
Published: 30 June 2021 in Journal of Cleaner Production
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With the development of the integrated energy Internet, energy structure optimization and emission reduction have led to higher requirements for developing various energy sources to enable coordinated and sustainable development. However, data-mining methods are rarely used to study the coordination of multi-energy generation in published research results. In this study, from the perspective of power industry emissions, coordinated generation of various energy sources, and balance of power generation and consumption, a data-mining algorithm was used to analyze the development of thermal power, hydropower, wind power, waste heat, gas, and other power sources. The chi-square automatic interaction detection tree (CHAID), logistic regression, and two-step clustering methods were applied. The results show that: a) CO2 and SO2 emissions were mainly affected by thermal power generation, whereas NOx emissions were jointly affected by thermal power, garbage power, and gas-fired power, and the emissions of various pollutants increased with an increase in power consumption. The optimal power-generation scheme under minimum emission can be obtained. b) There was a strong correlation between thermal power generation and residential electricity consumption, and renewable energy (wind energy, photovoltaic, hydropower) exhibited the highest correlation with the electricity consumption of the tertiary industry, which indicates that renewable energy generation can be promoted by managing electricity consumption in the tertiary industry. c) When the electricity demand of all users was small, the proportion of renewable energy power generation increased; in contrast, the thermal power generation was larger. This indicates the importance of improving the sustainable and stable power supply of renewable energy. This study provides a data analysis model for the coordinated development of multiple energies, which will contribute to the decision-making basis for controlling power emissions, improving the utilization rate of renewable energy, and optimizing the energy structure.

ACS Style

Dongfang Ren; Xiaopeng Guo; Cunbin Li. Research on big data analysis model of multi energy power generation considering pollutant emission—Empirical analysis from Shanxi Province. Journal of Cleaner Production 2021, 316, 128154 .

AMA Style

Dongfang Ren, Xiaopeng Guo, Cunbin Li. Research on big data analysis model of multi energy power generation considering pollutant emission—Empirical analysis from Shanxi Province. Journal of Cleaner Production. 2021; 316 ():128154.

Chicago/Turabian Style

Dongfang Ren; Xiaopeng Guo; Cunbin Li. 2021. "Research on big data analysis model of multi energy power generation considering pollutant emission—Empirical analysis from Shanxi Province." Journal of Cleaner Production 316, no. : 128154.

Journal article
Published: 27 January 2021 in Sustainability
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Starting from the perspective of the uncertainty of supply and demand, using the Copula function and fuzzy numbers a scenario generation method, considering the uncertainty of scenery, and a random fuzzy model of energy demand uncertainty are proposed. Then, through the energy flow direction and the energy supply, production, conversion, storage, and demand, a multi-objective model considering the economic and environmental protection of a park is constructed. Here, the park refers to a microgrid that gathers distributed energy such as wind and photovoltaics and has requirements for cooling, heat, and electricity at the same time. Next, combining the constraints of each link, the particle swarm algorithm is used to solve the model. Finally, an example is analyzed in a certain park. The results of the example show that, on the one hand, the proposed scenario generation method and fuzzy number method can reduce the uncertainty of supply and demand, effectively fitting the wind and photovoltaic output and various energy demands. On the other hand, considering the economy and environmental protection of the park at the same time, the configuration of energy storage equipment can not only improve the economy of the park, but also promote the consumption of renewable energy.

ACS Style

Shiping Geng; Gengqi Wu; Caixia Tan; Dongxiao Niu; Xiaopeng Guo. Multi-Objective Optimization of a Microgrid Considering the Uncertainty of Supply and Demand. Sustainability 2021, 13, 1320 .

AMA Style

Shiping Geng, Gengqi Wu, Caixia Tan, Dongxiao Niu, Xiaopeng Guo. Multi-Objective Optimization of a Microgrid Considering the Uncertainty of Supply and Demand. Sustainability. 2021; 13 (3):1320.

Chicago/Turabian Style

Shiping Geng; Gengqi Wu; Caixia Tan; Dongxiao Niu; Xiaopeng Guo. 2021. "Multi-Objective Optimization of a Microgrid Considering the Uncertainty of Supply and Demand." Sustainability 13, no. 3: 1320.

Journal article
Published: 27 August 2020 in Energy
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With the continuous consumption of fossil energy, the development and utilization of renewable energy with the goal of sustainable development has become more important in China, a large energy-consuming country. The purpose of this article is to develop a new framework to assess the priority of renewable energy development and utilization in China from a sustainable development perspective, thereby contributing to renewable energy management. To determine the weight of each criterion, the network analysis method (ANP) was used to evaluate the importance of each criterion. In addition, multi criteria decision making (MCDM) methods such as WSM, TOPSIS, PROMETHEE, ELECTRE and VIKOR were used to quantitatively evaluate renewable energy alternatives so that different methods can support each other to make the comprehensive results more convincing. The results show that energy sustainability indicators have the highest priority among all standards. Among renewable energy sources in China, hydropower is the best choice. From a regional perspective, North and Northeast China are biased towards wind power, East and Northwest China are biased towards photovoltaics, and Central South and Southwest China are biased towards hydropower. In the results of two sensitivity analyses, the sensitivity of energy efficiency, energy variability, and economic allocation is relatively high.

ACS Style

Tao Li; Ang Li; Xiaopeng Guo. The sustainable development-oriented development and utilization of renewable energy industry——A comprehensive analysis of MCDM methods. Energy 2020, 212, 118694 .

AMA Style

Tao Li, Ang Li, Xiaopeng Guo. The sustainable development-oriented development and utilization of renewable energy industry——A comprehensive analysis of MCDM methods. Energy. 2020; 212 ():118694.

Chicago/Turabian Style

Tao Li; Ang Li; Xiaopeng Guo. 2020. "The sustainable development-oriented development and utilization of renewable energy industry——A comprehensive analysis of MCDM methods." Energy 212, no. : 118694.

Journal article
Published: 11 June 2019 in Journal of Cleaner Production
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With the increasing consumption of fossil fuels, environmental problems are becoming increasingly serious. To solve these problems, we must vigorously develop clean energy. To this end, China has promulgated many policies to support the development of photovoltaic energy. To study the carbon footprint of the photovoltaic power supply chain and calculate the reduction of carbon emissions, this article establishes a carbon emission mathematical calculation model for photovoltaic power generation systems during the production, transportation and waste treatment processes. The carbon emission reduction model is established by calculating the power consumption of the photovoltaic power supply chain and power generation throughout the life cycle and by using the 1 kW photovoltaic power generation system as an example to analyse the data. The results show that from the perspective of the supply chain, it can effectively reduce carbon emissions from photovoltaic power supply chains by improving raw material development technology to provide high development efficiency, optimizing parts production processes, replacing transportation vehicles with electric vehicles gradually, increasing waste recycling efficiency, and improving waste disposal methods. In 2017, compared with thermal power generation in China, photovoltaic power generation systems were used in areas where the solar radiation is effective for 1000 h-3000 h, the CO2 emission reduction could be considered to be between 1.738 GT and 3.078 GT, which have shown good carbon emission reduction effect.

ACS Style

Xiaopeng Guo; Kai Lin; Han Huang; Yang Li. Carbon footprint of the photovoltaic power supply chain in China. Journal of Cleaner Production 2019, 233, 626 -633.

AMA Style

Xiaopeng Guo, Kai Lin, Han Huang, Yang Li. Carbon footprint of the photovoltaic power supply chain in China. Journal of Cleaner Production. 2019; 233 ():626-633.

Chicago/Turabian Style

Xiaopeng Guo; Kai Lin; Han Huang; Yang Li. 2019. "Carbon footprint of the photovoltaic power supply chain in China." Journal of Cleaner Production 233, no. : 626-633.

Articles
Published: 17 May 2019 in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
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The problem of wind curtailment is serious in China. It not only restricts the development of renewable energy, but also brings economic losses to the enterprises and the society. Unfortunately, the problem of wind power curtailment is difficult to eliminate under existing facilities and technical conditions. Therefore, it is a practicable way to control the wind power curtailment rate as far as possible on the premise of guaranteeing the profit of the power generation enterprise and meeting the demand for electricity users. In the context of the coordinated development of the Beijing-Tianjin-Hebei region, this article presents a deep analysis of other energy generation, power grid planning, social benefits, electricity prices, and other factors associated with the wind power generation, then we constructed a complete “cost of wind curtailment” model. Finally, an uncertainty analysis is added to show the impact of some uncertainties on the model, which makes the model more complete, and after calculating the cost and benefit of “reducing the rate of wind curtailment,” some pertinent suggestions are given, which will provide policy basis for the development of renewable energy.

ACS Style

Xiaopeng Guo; Dongfang Ren; Cunbin Li. Study on the problem of wind power curtailment in Beijing-Tianjin-Hebei based on risk-return. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2019, 42, 2632 -2647.

AMA Style

Xiaopeng Guo, Dongfang Ren, Cunbin Li. Study on the problem of wind power curtailment in Beijing-Tianjin-Hebei based on risk-return. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2019; 42 (21):2632-2647.

Chicago/Turabian Style

Xiaopeng Guo; Dongfang Ren; Cunbin Li. 2019. "Study on the problem of wind power curtailment in Beijing-Tianjin-Hebei based on risk-return." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42, no. 21: 2632-2647.

Research article
Published: 22 September 2016 in Environmental Science and Pollution Research
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This paper studies the relationship among carbon emissions, GDP, and logistics by using a panel data model and a combination of statistics and econometrics theory. The model is based on the historical data of 10 typical provinces and cities in China during 2005–2014. The model in this paper adds the variability of logistics on the basis of previous studies, and this variable is replaced by the freight turnover of the provinces. Carbon emissions are calculated by using the annual consumption of coal, oil, and natural gas. GDP is the gross domestic product. The results showed that the amount of logistics and GDP have a contribution to carbon emissions and the long-term relationships are different between different cities in China, mainly influenced by the difference among development mode, economic structure, and level of logistic development. After the testing of panel model setting, this paper established a variable coefficient model of the panel. The influence of GDP and logistics on carbon emissions is obtained according to the influence factors among the variables. The paper concludes with main findings and provides recommendations toward rational planning of urban sustainable development and environmental protection for China.

ACS Style

Xiaopeng Guo; Dongfang Ren; Jiaxing Shi. Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model. Environmental Science and Pollution Research 2016, 23, 24758 -24767.

AMA Style

Xiaopeng Guo, Dongfang Ren, Jiaxing Shi. Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model. Environmental Science and Pollution Research. 2016; 23 (24):24758-24767.

Chicago/Turabian Style

Xiaopeng Guo; Dongfang Ren; Jiaxing Shi. 2016. "Carbon emissions, logistics volume and GDP in China: empirical analysis based on panel data model." Environmental Science and Pollution Research 23, no. 24: 24758-24767.