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As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited's region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3. [ABSTRACT FROM AUTHOR]

A significant corridor under the "Belt and Road Initiative" is announced by the government of China for numerous transport and energy projects. China focuses greatly on the sustainability of these projects, in accordance with Green Investment Principles. For this, environmental, social, governance, and technological considerations are taken into account. This paper offers a novel assessment of the impact of green sustainability under BRI and the current emission scenario in Pakistan. The question that needs to be addressed here is whether the projects under Belt and Road Initiative are as environment friendly as they claim. In the first phase of this study, we review theories establishing evidence based on existing literature to determine the influence of Belt and Road on economic development. In the next phase, we use Environment Kuznets Curve and apply estimates from Johansson's Cointegration Test to quantify the present environmental scenario of Pakistan and probable environmental risk portrayed by the China-Pakistan Economic Corridor (CPEC). This paper helps in determining the relationship between carbon emission and three major variables including energy consumption, economic growth, and foreign direct investment (FDI). Cointegration analysis is applied on time series data comprising of a 40 years' period from 1979 to 2019. The paper finds that (i) the belt and road corridors could substantially improve trade, foreign investment, and living conditions for citizens in participating countries; (ii) moreover, this study confirms the presence of one cointegrated equation suggesting the presence of a long term relationship between the CO2 and independent variables and a short run association running from EnC to CO2. The study also proposes a Green BRI Model which formulates a body that devises environmental performance standards, reviews procedures, and ensures that they get implemented on all CPEC projects. The Belt and Road Initiative increases emissions among participating countries. China and corridor economies need to adopt strict policy reforms that increase transparency, expand trade, improve debt sustainability, and mitigate environmental, social, and corruption risks. [ABSTRACT FROM AUTHOR]

The household energy consumption has been a hot field in the study of household energy consumption in recent years. With the increase of residents' income level and the pushing of urbanization, there is a complex nonlinear relationship between energy price and energy consumption. The purpose of this paper is to investigate the scenario effect of per capita income and regional differences in urbanization development on the relationship between electricity sales price and urban household electricity consumption. To this direction, based on the regional characteristics of economic development in China, with the residents' disposable income and the urbanization level as the conversion variables and the electricity sales price as the core explanatory variable, the panel smooth transition regression (PSTR) model of electricity sales price and urban household electricity consumption from the perspectives of income level and urbanization has been constructed in this paper. The empirical results show the following: (1) Under the consideration of regional difference of residents' income level, with the increase of residents' disposable income level, there is a significant negative correlation between electricity sales price and urban household electricity consumption in the whole country, the eastern region, and the central region, while such correlation is significantly positive in the western region. (2) Under the consideration of the difference of urbanization development level, the national regional electricity sales price and the urbanization level are positively related to the urban household electricity consumption, and the urbanization level in the western region plays the biggest role in promoting the urban household electricity consumption, followed by the eastern region and then the central region which plays the smallest role. This paper discusses the effect of electricity sales price on urban household electricity consumption from the perspective of regional difference in income and urbanization, which provides the decision-making basis and empirical support for developing regional electricity price policy and household energy consumption policy. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of new energy resources being generated in the power system, it is necessary to plan the capacity configuration of the power supply side through the coordination of power generation, grid, load, and energy storage, to create a relatively controllable power generation output and ensure the safe and stable operation of the power system is a key problem that needs to be solved at present. Therefore, combined with national and regional policies and resource constraints in China, this paper firstly determines the requirements and boundary conditions of various power supply planning in the regional power system and proposes a "generation-grid-load-energy storage" coordination mode. Secondly, establish a regional power system coordination planning model considering the proportion of new energy consumption and low-carbon cost. Finally, in three scenarios with a high, medium, and low proportion of new energy resources, the new installed capacity configuration of regional power supply is optimized to verify the effectiveness of the proposed model and provides reference for regional power supply planning. [ABSTRACT FROM AUTHOR]

With the increasing demand for energy and with the increasing awareness of environmental protection, clean energy will become the ultimate solution of energy in the near future. In order to meet the future demands of clean energy, it is important to accurately predict the consumption of clean energy. This paper is based on the total consumption data of clean energy in China from 2000 to 2019, including natural gas, hydropower, nuclear power, and wind power consumption. The combined model is used to predict the clean energy consumption and achieves the optimal prediction with minimum variance. The results show that compared to the GM(1,1) and ARIMA model, the combined forecasting model has lower relative error when fitting and predicting the consumption demand of clean energy. It is observed from the prediction results that the clean energy consumption would have a rapid growth tendency, and the growth rate will be about 8.6% in the next five years. The consumption would be about 1.7 billion tons standard coal in the year of 2024. [ABSTRACT FROM AUTHOR]

Ecological deterioration, air pollution, and resource depletion have shrouded the vast regions of China, raising widespread concerns about the sustainable development of the domestic economy. Although financial agglomeration has become a pivotal approach for China to realize green transformation, there is a lack of evidence against the causal correlation between financial agglomeration and sustainable development of the regional economy. To fill this gap, using the data of 29 provincial capital cities in China spanning from 2009 to 2019 and adopting individual time bidirectional fixed effect model, IV-GMM approach, and alternative modeling techniques, this paper investigates the impact of financial agglomeration on sustainable development of the regional economy for the first time. The results indicate that there is a significant positive correlation between financial agglomeration and the sustainable development of the regional economy. Financial agglomeration facilitates the improvement of regional energy efficiency, and the latter further mediates the relationship between financial agglomeration and sustainable development of the regional economy. In addition, the empirical results also demonstrate that the higher the economic policy uncertainty, the weaker the positive relationship between financial agglomeration and energy efficiency. The present study is of great significance for China to implement energy-saving and emission-reduction tasks and achieve sustainable urban construction. [ABSTRACT FROM AUTHOR]

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country's medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China's per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China's per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China's per capita living energy consumption. The results show that the proposed model is more suitable for predicting China's per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China's per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers. [ABSTRACT FROM AUTHOR]

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country's medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China's per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China's per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China's per capita living energy consumption. The results show that the proposed model is more suitable for predicting China's per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China's per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers. [ABSTRACT FROM AUTHOR]

Environmental issues caused by energy consumption have attracted increasing attention recently. Shanxi Province, a typical energy-dominated region in China, has long-term dependency on coal industry generating extensive economic growth, which is detrimental to green development. Distinguished from previous studies ignoring driving factors of energy consumption, this paper establishes a vector autoregression (VAR) model to dynamically identify the drivers of energy consumption based on STIRPAT model in Shanxi Province from 1990 to 2015. It can be obtained from the impulse response analysis that a positive shock in population, GDP, and urbanization level, respectively, positively affect energy consumption, and a positive change in technology negatively affects energy consumption in the long run. The variance decomposition results indicate that fluctuation in energy consumption explained by the innovation of the urbanization level accounts for 23.18%, which plays a prevailing role in increasing energy consumption. Meanwhile, the forecasting results of GM (1,1) model manifest that energy consumption in Shanxi Province generally has an increasing trend from 2016 to 2025. Consequently, Shanxi can achieve green development through optimizing energy structure, promoting the transformation of resource-based cities, and promoting low-carbon technological innovation. This paper can be available for other resource-based regions analogous to Shanxi. [ABSTRACT FROM AUTHOR]

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country's medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China's per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China's per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China's per capita living energy consumption. The results show that the proposed model is more suitable for predicting China's per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China's per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers. [ABSTRACT FROM AUTHOR]

Energy recovery and hybrid power technology are new directions in construction machinery energy-saving research. Thus far, however, no uniform standard method exists to evaluate the efficiency of novel energy-saving concept systems in early stages of development. Efficiency assessment is valuable and credible only by relying on driving cycles that are consistent with actual data. As representative products of construction machinery, hydraulic excavators are multifunctional, object-uncertain, and heavily influenced by operator habits. It is therefore challenging to develop standard excavator driving cycles. Aiming at the energy efficiency evaluation of new energy-saving products characterized by energy recovery and power system optimization, taking medium-sized hydraulic excavator as an example, this paper proposes an evaluation method of energy-saving efficiency and a classification construction method of comprehensive driving cycle of hydraulic excavator based on actual operating data with load demand power and boom recoverable power as combined variables. First, based on the analysis of general driving cycle variables for excavators with different energy-saving schemes, a load demand power model and boom recoverable power model based on machine sensing data are established. Second, 10 excavators were selected at different locations in southern China, and 30 days of real-world working data were recorded. Third, according to the periodic characteristics of working data, a method for dividing microcycle operation structure is proposed. The microcycle sample space based on the data conversion of working data is constructed and classified via the clustering algorithm. Finally, a comprehensive excavator driving cycle based on the classification results is constructed through the Markov method. Results show that the energy-saving efficiency of the three classification driving cycles was 17.45%, 13.60%, and 11.88%, respectively; the comprehensive energy-saving efficiency was 15.76%. The deviations in maximum load demand power and maximum boom recoverable power between the constructed comprehensive cycle and the sample space were 7.81% and 8.61%; the average deviation of characteristic parameters was 4.26%. The comprehensive driving cycles can fairly reflect the general characteristics of real-world working conditions. The proposed construction method of comprehensive driving cycles based on sample space is therefore reliable and holds great promise for evaluating the energy-saving efficiency of new energy-saving concept systems. [ABSTRACT FROM AUTHOR]

Under the situation of global low-carbon development, the contradiction among energy consumption, economic growth, and CO2 emissions is increasingly prominent. Considering the possible two-way feedback among the three, based on the panel data of 30 regions in China from 2000 to 2017, this paper establishes a spatial Durbin model including economic growth, energy consumption equation, and CO2 emissions and studies the dynamic relationship and spatial spillover among economic growth, energy consumption, and CO2 emissions effects. The results show that the economic growth can significantly improve carbon dioxide emissions, and China's economic growth level has become a positive driving force for carbon dioxide emissions. However, economic growth will not be significantly affected by the reduction of carbon dioxide emissions. There is a two-way relationship between energy consumption (ENC) and carbon dioxide emissions (CO2). Energy consumption and carbon emissions are interrelated, which has a negative spatial spillover effect on the carbon dioxide emissions of the surrounding provinces and cities. [ABSTRACT FROM AUTHOR]

The hilly farmland in China is characterized by small farmland areas and dense farmland distribution, and the working environment is three-dimensional topographic farmland, so the working conditions in the field are relatively complex. In this working environment, the coverage path planning technique of a farmland autonomous task is harder than that of 2D farmland autonomous task. Generally, the path planning problem of 2D farmland is to construct the path cost model to realize the planning of agricultural machinery driving route, while for the path planning problem of three-dimensional terrain farmland in the hilly region, this paper proposes a covering path planning scheme that meets the requirements of autonomous work. Based on the energy consumption model, the scheme searches the optimal driving angle of agricultural machinery, prioritizes solutions to the problem of covering path planning within the scattered fields in the working area, and then searches through the genetic algorithm for the optimal order of traversing the paths of each field to complete the coverage path planning in the working area. On the one hand, the scheme optimizes the planning route in the fields from the angle of optimal energy consumption; on the other hand, through the genetic algorithm, the fields are connected in an orderly manner, which solves the comprehensive problems brought by the unique agricultural environment and farming system in China's hilly areas to the agricultural machinery operation. The algorithm program is developed according to the research content, and a series of simulation experiments are carried out based on the program using actual farmland data and agricultural machinery parameters. The results show that the planned path obtained at the cost of energy consumption has a total energy consumption of 4771897.17J, which is 17.4% less energy consumption than the optimal path found by the path cost search; the optimization effect is evident. [ABSTRACT FROM AUTHOR]

Taking Hunnan District of Shenyang City as an example, this paper investigates 32 government agencies, 17 tertiary first-class hospitals, and 15 colleges and universities in Hunnan region and analyzes the energy consumption characteristics of three types of public institutions. Through the use of DeST energy consumption software to simulate the energy consumption of three types of public institutions, combined with the load density method to predict the total energy demand of hospitals, universities, and government agencies in Hunnan area and the MARKAL model coupling mechanism, the coupling model of active and passive energy suitable for public institutions is presented. According to the forecast results of energy demand, combined with the energy consumption structure and energy conversion efficiency, the energy supply of each subitem of the public institution was solved and the energy supply of the public institutions in Hunnan District was optimized in accordance with the relevant constraints. Through calculation, it is found that by 2020, the passive energy supply of public institutions in Hunnan District will account for 19.79% and active energy sources such as coal and petroleum will still be the main type of energy used by public institutions. Based on the research on the active and passive energy coupling supply of public institutions in Hunnan District, the active and passive energy coupling model and algorithm of public institutions constructed can provide a reference basis for the active and passive energy coupling utilization of public institutions in China. [ABSTRACT FROM AUTHOR]

Based on the dynamic characteristics of enterprises' competition and cooperation, this paper introduces the idea of ecology and synergy and constructs a dynamic coevolution model of financing allocation efficiency and financing niche based on improved Lotka-Volterra model. The parameters in the model are difficult to be given by the least square method and the maximum likelihood estimation method; the accelerated genetic algorithm is proposed to solve the parameters in the dynamic coevolution model, which makes the parameter estimation more accurate and reasonable. Finally, the data of new energy vehicle listed companies in China from 2009 to 2017 are given to validate the proposed model, and the dynamic process of coevolution of new energy vehicle enterprises, capital allocation efficiency, and financing niche is described. The results show that the minimum financing niche breadth of industry and market determines the location of the equilibrium point. With capital allocation efficiency as the core, adjusting the financing niche through financing market, industrial policy, enterprise development, and other factors will help to improve the coevolution balance between industrial capital allocation efficiency and financing niche and promote the coordinated development of strategic emerging industries. [ABSTRACT FROM AUTHOR]

Water pollution control is currently an important development strategy for China. Analysis and forecasting of wastewater discharge and the energy consumption are critical for this strategy. In this paper, we proposed the two time series models, namely, improved GM(0,n) model (IGM(0,n)) and optimized FGM(1,1) model (OFGM(1,1)). Two numerical experiments were used to test the validity of these models. It was indicated that the forecasting ability of two models had remarkably improved, compared to traditional FGM(1,1) and GM(0,n), respectively. We applied the two models to forecast wastewater discharge and energy consumption in China. The forecasting results have shown that the current growth rate in treated wastewater could meet the planning target of the 13th Five-Year Plan. The wastewater discharge and the energy consumption would reach 77.39 billion tons and 22.75 billion kWh, respectively, in 2020. [ABSTRACT FROM AUTHOR]

In recent years, gas has begun to be widely used in power generation as well as in manufacturing because of the environmental advantages, lower cost, and the relative safety and reliability. Accurate prediction of future gas consumption is of great importance for energy security. In this paper, we first use path analysis to select the core factors that impact gas consumption, then use the RBFQRNN model and BVAR model separately to predict future gas consumption, and finally discuss and compare the results from the two models. The results show that if there is a positive growth in GDP, urbanization, and population, then gas consumption is expected to increase over a comparatively long time. In detail, gas consumption is expected to remain at about 25% of all energy consumption, GDP's contribution to gas consumption is expected to be about 53%, and the urbanization rate contribution would be about 15%. Gas consumption in China is predicted to reach around 178649.23 million cubic meters by the end of 2015 and about 264698.86 million cubic meters by 2020. [ABSTRACT FROM AUTHOR]

With the rapid economic development, the transportation sector becomes one of the high-energy-consumption and high-CO2- emissions sectors in China. In order to ensure efficient use of energy and to reduce CO2 pollution, it is important to gain the best performance standards in China's transportation sector. Data envelopment analysis (DEA) has been accepted as a popular tool of efficiency measurement. However, previous studies based on DEA are mainly restricted to the radial expansions of outputs or radial contractions of inputs. In this paper, we present a nonradial DEA model with multidirectional efficiency analysis (MEA) involving undesirable outputs for the measurement of regional energy and environmental efficiency of China's transportation sector during the period 2006-2010. We not only evaluate the energy and environmental efficiency level and trend of China's transportation sector but also investigate the efficiency patterns of 30 regions and three major areas of China. Additionally, we identify the energy saving potential and CO2 emissions reduction potential for each province and area in China in this study. [ABSTRACT FROM AUTHOR]

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development. [ABSTRACT FROM AUTHOR]

This paper employs an extended Kaya identity as the scheme and utilizes the Logarithmic Mean Divisia Index (LMDI II) as the decomposition technique based on analyzing CO2 emissions trends in China. Change in CO2 emissions intensity is decomposed from1995 to 2010 and includesmeasures of the effect of Industrial structure, energy intensity, energy structure, and carbon emission factors. Results illustrate that changes in energy intensity act to decrease carbon emissions intensity significantly and changes in industrial structure and energy structure do not act to reduce carbon emissions intensity effectively. Policy will need to significantly optimize energy structure and adjust industrial structure if China's emission reduction targets in 2020 are to be reached. This requires a change in China's economic development path and energy consumption path for optimal outcomes. [ABSTRACT FROM AUTHOR]