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In recent years, extreme rainfall events have frequently occurred frequently, and heavy rainfall can cause drastic changes in the troposphere. Therefore, achieving to achieve real-time high-precision numerical prediction of key tropospheric parameters during heavy rainfall has become a major problem in global navigation satellite system (GNSS) meteorology. In this paper, two extreme rainfall events in southern China (Guangdong region) and northern China (Shandong region) in 2022 are used as case studies. Twenty-four-hour real-time numerical forecasts of key tropospheric parameters (atmospheric weighted mean temperature (Tm), precipitable water vapor (PWV), and GNSS zenith tropospheric delay (ZTD)) are obtained using three models, namely, the HGPT2, GPT3, and WRF models. Two optimization models, i.e., WRFDA (am) and WRFDA (pre), are then constructed by assimilating two types of data (global upper air and surface weather observations and daily advanced microwave sounding unit A (AMSU-A) brightness temperature) based on the WRF model. The experimental results for heavy rainfall show that (1) the WRF model predicts the key tropospheric parameters with better accuracy than the HGPT2 and GPT3 models, and the WRFDA (pre) model predicts PWV and ZTD with the highest accuracy; (2) the WRFDA (pre) model achieves a higher accuracy than the WRF model in predicting PWV and ZTD, where the PWV prediction accuracy is improved relative to the WRF model (in the south: MAE: 32.7 %; RMSE: 33.9 %; MAPE: 36.8 %; in the north: MAE: 27.3 %; RMSE: 24.2 %; MAPE: 28.0 %); this model achieves an MAE of 2.17 cm and an RMSE of 2.70 cm in 24-h ZTD prediction in the south, while the MAE reaches 2.48 cm, and the RMSE is 3.18 cm in the north; (3) the models provide a higher forecast accuracy in the southern region than in the northern region for heavy rainfall. The WRFDA (pre) model provides a favourable ZTD accuracy at GNSS stations near the ocean, while the WRFDA (am) model provides a satisfactory ZTD accuracy at inland GNSS stations, and the WRFDA (am) model provides the highest ZTD prediction accuracy at GNSS stations above 100 m. [ABSTRACT FROM AUTHOR]

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy. • A pseudo-local reserve market is established considering EV's reserve potential. • Optimal bidding strategy for DSO in China's electricity markets is constructed. • A framework for EV coordinated V2G operation is developed. • The proposed method can limit load deviation cost for more profits. [ABSTRACT FROM AUTHOR]

• From the perspective of spatial cross-sectional data, this study explores causal inference in the ecological environment. • Two primary causal pathways are identified. • The study unveils the intricate mechanisms among diverse factors within the ecological environment system. The ecological environment is closely related to natural conditions, climate change, and human activities. However, existing research predominantly concentrates on evaluating the direct impacts of various factors on the ecological environment, yet lacks an in-depth exploration of their causal relationships and pathways. This paper introduces a new perspective to research in this field by incorporating spatial cross-sectional data analysis methods. The study utilized the 2020 China's High-resolution Eco-Environmental Quality (CHEQ) dataset to evaluate the spatial differentiation characteristics of Ecological Environmental Quality (EEQ) within the western urban agglomeration of Guangdong Province. A framework of "terrain-urbanization-climate" influencing factors was constructed, and the Geographical convergent cross mapping (GCCM) method was employed to infer the causal relationship between EEQ and influencing factors. Additionally, the spatial distribution characteristics of influencing factors on ecological environment quality were studied using the Multiscale Geographically Weighted Regression Model (MGWR). The results indicate that seven influencing factors including Elevation, Population, Precipitation, TEMP, TMMX, TMMN, and Slope have clear causal relationships with EEQ and exhibit bidirectional coupling relationships. There exist complex causal relationships among influencing factors, revealing the interaction mechanisms and causal pathways among multiple factors in the ecological environment system. These analyses enrich our understanding of the intricate and complex relationships between the ecological environment and influencing factors, providing a valuable reference for ecological protection and sustainable development. [ABSTRACT FROM AUTHOR]

In the context of China's dual‑carbon goals and electricity market reforms, the high penetration of renewable energy sources (RES) in a single microgrid (MG) imposes challenges to its flexible operation. This paper addresses the cooperation within a virtual power plant (VPP) aggregated by multiple MGs. The VPP, managed by the VPP operator, acts as an intermediate entity to facilitate MGs' economic and low-carbon operation. To ensure the interests of various stakeholders in the VPP, this work proposes a low carbon oriented tri-stage collaborative energy management framework. In the 1st stage, the day-ahead optimal scheduling of individual MGs and the bidding strategy of the VPP operator are modeled, where an internal pricing method is incorporated. The 2nd stage addresses the real-time redispatch of the VPP operator using a receding horizon-based model predictive control (MPC) solution. This aims to satisfy the real-time operational requirements of MGs and reduce adjustment costs. Furthermore, in the 3rd stage, a penalty allocation mechanism is established for MGs based on their forecasting errors, to protect the VPP operator's interests. The effectiveness of the proposed solution is evaluated through comparative simulations in the context of the Guangdong electricity market. • The virtual power plant (VPP) aggregated by multiple microgrids (MG) is studied. • The quantification of carbon emission is involved in the whole scheduling process. • An peer-to-peer (P2P) electricity trading pricing method is improved and adopted. • A penalty allocation mechanism is proposed to guarantee interests of each entity. • The Guangdong electricity market is considered as the market mechanism. [ABSTRACT FROM AUTHOR]

Rapid urbanization results in energy shortage and unreasonable energy metabolism structure. The goal of this research is to take Guangdong, China as a special case study to illustrate the influence of energy classification differences on the energy metabolism system. To do so, we introduced the concept of "urban metabolism", and treated sectors and energy flows as nodes and paths in a network model. We used the input output analysis to compile the physical input output table through the embodied energy element intensity. Building on this, we used ecological network analysis to quantify the urban metabolic processes and energy metabolism levels within the urban system. In addition, the alternative indicators are first introduced to explore the best alternative energy in various sectors to avoid a short-term energy crisis. In this paper, different energy groups are considered on the energy metabolism system, including all energy, primary energy, secondary energy and six specific energy, which will fill the research gap about the influence of energy classification on urban energy metabolism. It is found that the energy metabolism of Guangdong is now in the state of sub-health. The energy metabolism hierarchy atlas shows that the pulling force is hardly affected by the energy classification but the driving force is very sensitive, which further illustrate that producers can choose different energy according to their production structure. Facing the shortage of energy supply, the secondary energy with the highest substitutability of all energy value is the better alternative energy for advanced manufacturing sector (containing twelve industries such as Manufacture of transportation equipment and Manufacture of metal products). More specifically, the nonrenewable properties of primary energy make it particularly important to find the corresponding alternative energy. Heat is the best alternative energy for crude oil, while electricity is irreplaceable. It is expected that the results will provide scientific support to guide the reform of urban energy metabolic system in an attempt to coordinate the energy development strategy, improve the energy consumption structure and maintain energy security and stability. • An Energy Metabolism Network (EMN) model from a Guangdong perspective is developed. • Different energy types have a great impact on the level of energy metabolism. • The inherent mechanism of energy ecosystems are revealed by the energy hierarchy atlas. • In-depth analysis is conducted to investigate the energy interactions between different industries. • Scientific basis for supporting the formulation of the effective reform of urban energy metabolism system. [ABSTRACT FROM AUTHOR]

Site selection is a key factor to the success of ocean thermal energy conversion (OTEC) project. It is a multi-criteria decision making (MCDM) problem with a series of conflicting criteria involved. However, limited studies were found concerning this area and problems existing in classical MCDM methods tended to fail OTEC site selection decisions. Therefore, this paper proposes an extended MCDM method and establishes a new decision-making framework to select the optimal location. First, a comprehensive evaluation criteria system for OTEC site selection from the view of sustainability with 16 criteria involved is established. Second, feasible alternative sites for OTEC project are identified by PEST analysis. Third, an extended PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) under intuitionistic fuzzy environment is proposed to avoid compensation assumptions. During the decision process, seven groups of experts shared the work and cooperated with each other according to their backgrounds. Finally, a real case is studied and sensitive analysis is conducted, demonstrating the robustness and effectiveness of the proposed method. This study can effectively improve OTEC site selection decision making quality and also extends the domains of intuitionistic fuzzy PROMETHEE applications. • A novel multi-criteria decision-making framework for site selection is proposed. • The criteria system for ocean thermal energy conversion site selection is constructed. • A case study is carried out in areas of Guangdong and Guangxi Provinces in China. [ABSTRACT FROM AUTHOR]

Abstract Manufacturing industry is an important part of economic development. Recognizing the sustainable development of regional manufacturing has an important guiding role in the formulation of environmental policies. Based on the meta-frontier Malmquist-Luenberger (MML) productivity index, this paper uses Chinese provincial panel data (2003–2015) to measure the green total factor productivity (TFP) of China's manufacturing. It is decomposed into the following: efficiency change (EC), change of the best practice gap ratio (BPC) and change of technical gap ratio (TGC). We compared the regional differences in green TFP growth in manufacturing in all provinces, analyzed regional and time differences in green TFP and the decomposition of manufacturing between regions and identified innovative regions. The results show that: (1) The average annual growth rate of green TFP in Chinese manufacturing is 0.85% with technological progress playing a key role. (2) There are differences in the causes of the increase in green TFP in all regions. The eastern region and the western region are driven by technological progress while the central region depends on technology catch-up effects. (3) Guangdong and Qinghai are two different types of global innovation regions with Guangdong driven by technological innovation and Qinghai driven by strict environmental protection systems. [ABSTRACT FROM AUTHOR]

• We compared the structural characteristics of 18 urban agglomerations in China. • Our framework contained analysis of 3 different scales based on 4 elements' flows. • Guangdong-Hong Kong-Macao urban Agglomeration had the greatest average flow (5.15). • Shandong Peninsula Urban Agglomeration had the best performance in overall network. • Cities with high centrality were in the bidirectional or main spillover subgroups. Flows of different elements among cities can affect urban organizational structures, leading to specific links between and roles for cities within urban agglomerations. Previous studies have mainly focused on flows produced by specific elements in a single urban agglomeration, and they cannot reveal differences in organizational structures affected by multiple elements among different urban agglomerations. Therefore we selected 18 typical urban agglomerations in China and explored their structural characteristics under comprehensive effects of four elements: economy, resources, population and technology, with gravity model and social network analysis. Guangdong-Hong Kong-Macao Urban Agglomeration had a maximum flow intensity of 5.15, this concentration range was the largest from 0.65 to 3.75, indicating that more elements flowed internally. Shandong Peninsula Urban Agglomeration had the largest network density at 0.412, the lowest efficiency at 0.567, and the lowest hierarchy at 0.000, showing that intercity relationships within SP were the tightest among the18 urban agglomerations. Geographical proximity and topological structure of subgroups proved that connections among subgroups were not complex, and cities of different provinces lacked close interactions. Furthermore, most of the key cities with high centrality indexes were municipalities directly under the Central Government or provincial capitals; these cities played important roles in networks and were distributed into "main spillover" and "bidirectional spillover" subgroups. This paper provides a unified analytical framework aimed at urban agglomerations that accounts for flows of multiple types of elements and explores urban organizational structures, and our results could provide scientific guidance for promoting the coordinated and sustainable development of urban agglomerations. [ABSTRACT FROM AUTHOR]

The coastal area of Guangdong Province underwent an extreme rainstorm in Aug. 2018. In this paper, using the convection-permitting WRF simulation results, a comparative analysis was given to the cloud microphysical conversion processes simulated under four cloud microphysics schemes, i.e. WSM6, WDM6, WSM7 and WDM7, as well as the accompanying latent heat budget. The maintenance mechanism of the rainstorm is discussed. In all four schemes, water vapor condensing into cloud water and accretion of cloud water by rain water were the top two conversion processes. In WSM6/WDM6 scheme, the third largest conversion process was accretion of rain water by graupel; in WSM7/WDM7, however, the third largest was cloud water evaporating to water vapor. The most important source of rain water was accretion of cloud water by rain, but the differences in hydrometeors conversion between rain water and graupel, snow and hail were the leading cause of differences in simulated rainfall under different schemes. The top three releasing latent heat processes were water vapor condensing into cloud water, water vapor deposited to cloud ice, and snow sublimation. The top two absorbing latent heat processes were cloud water and rain water evaporating to water vapor. In WSM6/WDM6/WSM7 schemes, the third major process of absorption of latent heat was snow sublimating to water vapor, in WDM7, however, it was hail melting into rain water. Compared with ice-phase processes, liquid-phase processes provide more latent heat. Differences in the release and absorption of latent heat were found in the mid-high troposphere. Hydrometeors in the troposphere maintain efficient conversion and release large amounts of latent heat that formed positive feedback with convection, which was the critical reason for the development and maintenance of the extreme rainstorm. • The top two microphysical conversion and latent heat processes of four schemes were consistent. • Conversion between rain water and graupel, snow and hail led different rain intensity. • Liquid-phase processes provided more latent heat than ice-phase processes. • Differences in latent heat processes were mainly found in the mid-high troposphere. • Rapid conversion of particles and latent heat processes formed positive feedback with convection. [ABSTRACT FROM AUTHOR]

Community shared solar (CSS) program has great potentials in contributing to the complete decarbonization of the power sectors worldwide. Few research have focused on its potential diffusion from the user perspective. This paper fills the gap by conducting a survey-based unlabeled choice experiment in the Guangdong Province, China to investigate the determinants of potential users' willingness to participate in CSS programs. We analyzed the survey data using a random parameter logit model. Our results show that users are more likely to participate in CSS programs with lower upfront payment, trialability, shorter payback period, a public utility as provider and larger impact on climate change mitigation. However, they are likely to accept longer payback period in CSS programs with higher participation rate because of the potential benefit from interpersonal communication. Users' previous knowledge of CSS programs and income will also increase their willingness to participate in CSS programs with higher upfront payment. Our results provide important implications for designing policies and business models to promote the distributive renewable energy in China. • Community shared solar is widely accepted among surveyed individuals in our choice experiment. • The business model, and the ownership structure of community shared solar affect its potential adoption. • The interpersonal communication and public promotion of community shared solar program can increase its popularity. [ABSTRACT FROM AUTHOR]

Abstract: This paper is the second in a series of three papers aimed at understanding the emissions of vehicles in China by conducting on-board emission measurements. This paper focuses on light-duty gasoline vehicles. In this study, we measured 57 light-duty gasoline vehicles (LDGVs) in three Chinese mega-cites (Beijing, Guangzhou, and Shenzhen), covering Euro 0 through Euro IV technologies, and generated CO, HC, and NOx emission factors and deterioration rates for each vehicle technology. The results show that the vehicle emission standards have played a significant role in reducing vehicle emission levels in China. The vehicle emission factors are reduced by 47–81%, 53–64%, 46–71%, and 78–82% for each phase from Euro I to Euro IV. Euro 0 vehicles have a considerably high emission level, which is hundreds of times larger than that of Euro IV vehicles. Three old taxis and four other Euro I and Euro II LDGVs are also identified as super emitters with equivalent emission levels to Euro 0 vehicles. Of the measured fleet, 23% super emitters were estimated to contribute 50–80% to total emissions. Besides vehicle emission standards, measures for restricting super emitters are equally important to reduce vehicle emissions. This study is intended to improve the understanding of the vehicle emission levels in China, but some key issues such as emission deterioration rates are yet to be addressed with the presence of a sufficient amount of vehicle emission measurements. [Copyright &y& Elsevier]

This paper examines the risk spillover effect between the carbon market and the stock market in China and the role of corporate social responsibility (CSR) on this effect. Employing Beijing, Hubei, and Guangdong carbon markets, we apply time-domain and frequency-domain spillover approaches and find that during the Chinese stock market crisis in 2015, risk spillovers from the stock market to the carbon market were more pronounced. Additionally, CSR firms are more dominant as information transmitters than those non-CSR (NCSR) firms in the carbon market. However, plausibly, due to the infancy of carbon trading, our results show that the level of connectedness between the carbon market and the stock market in China is relatively low. [ABSTRACT FROM AUTHOR]

• The new power supply structure resulting from the new policy has been considered. • The new models project the inefficient points onto a more realistic efficient frontier. • The new models have the power to allocate the shared resources. • Results show that the power production stage is allocated less shared resources. • The power sale stage's efficiency is lower than the one of power production stage. To relieve the problems of energy shortage and environmental pollution, many countries have implemented a variety of policies to improve the energy efficiency of the power industry. To understand the impacts of such policies on energy efficiency, this paper constructs novel network data envelopment analysis models to recognize the policy-induced structural changes in the power system. Compared with previous studies, the new models project the inefficient points onto a more realistic efficient production frontier, considering how the distribution proportion of shared resources will change when the power system, modeled as a power generation stage and a power sale stage, is led by the latter stage. Our empirical study on China's power system shows that the province of Guangdong and the eastern region of China perform best, and all power production stages perform better than the corresponding power sale stages. The development of the two power subsystems in the four regions is unbalanced. More than half of the power supply systems choose to allocate more proportion of the shared resources to the sale stage rather than use a more balanced distribution between the two stages. [ABSTRACT FROM AUTHOR]

As a new clean and sustainable energy that can reduce carbon emissions, offshore wind energy has received much more attention in recent years. In response to the complexity and flexibility issues in the decision-making process of offshore wind power investment, this paper proposes an investment decision framework based on a bidimensional binominal lattice that embeds deferral options, aiming to derive the appropriate development timing and investment potential for corporate investors in uncertain environment of market and technological development. And the investment model embedding revenue from carbon emission reduction highlights the advantages of clean energy. Furthermore, we creatively compare the policy effects of three subsidy schemes using the model as a policy analysis tool and use scenario analysis to simulate the impacts of dynamic adjustments in subsidies on investment decisions. For the case of an offshore wind power plant in Guangdong province, China, we find that the local government has to provide a subsidy of 0.11 CNY/kWh to allow the project to be profitable. In the absence of subsidies, however, it is preferable to defer the decision of investment with a wait-and-see attitude. Given the same total government financial input, we find that it will be more beneficial for the government to provide R&D subsidy than electricity tariff subsidy and operation subsidy in terms of long-term sustainable development. Our findings also demonstrate that considerations in the carbon trading market will also contribute to the development of offshore wind power. The conclusions of this article can provide valuable guidelines for corporate investors to adjust the appropriate development timing and for policymakers to optimize subsidy allocation, so as to promote the healthy development of offshore wind power and low-carbon transition. [ABSTRACT FROM AUTHOR]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

• The effects of cultivated land protection policies in different periods are compared. • Comprehensively considering the 12 factors affecting the CLPP. • The AHP-Entropy method was proposed to construct the cloud model of weight. • A normal cloud model was established. • An application for assessing the CLPP was developed by using the cloud model. The evaluation of the effects of cultivated land protection policies (CLPP) is an important issue in China's food security and land policy regulation. This article first analyzes the historical evolution of CLPP in China and divides it into five stages. Based on this, this paper proposes a CLPP implementation effect evaluation framework based on the perspective of farmers' perception. Taking Xingning City, Guangdong Province as the research area, the AHP-entropy method and cloud model are used to evaluate the regional CLPP implementation effect. Results show that the comprehensive effect of CLPP is between "middle" and "good," and specifically close to the "good" level. After the implementation of CLPP, its ecological management and protection, agricultural production enthusiasm, and farmers' satisfaction levels are generally at a medium or low level, and only the amount of cultivated land protection is at a medium to high level. Therefore, the formulation of CLPP in the future needs to start from the perspective of improving the comprehensive interests of farmers and enhance the focalization of policies. The cloud model realizes the uncertainty mapping of each evaluation index, takes into account the randomness and ambiguity of evaluation index quantification and grade division, and makes the evaluation result objective. This research method can serve as a reference for relevant land use policy evaluation. [ABSTRACT FROM AUTHOR]

Abstract: To find the key impact factors of CO2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO2 emissions. [Copyright &y& Elsevier]

Abstract: Vegetative riparian buffers are considered as key zones in providing natural regulation functions for water quality aimed at intercepting non-point source (NPS) nutrients from upslope areas to surface water. The function for reducing NPS nutrients may perform better in some locations than in others because not all factors affecting nutrient mitigation are equal in all locations. This paper intended to develop a spatial multi-criteria planning scheme involving nutrient export and buffer characteristics to evaluate the buffer restoration priorities. The Liuxihe River basin, located in central Guangdong Province, was used as a case study area for this research. Six indicators, including total nitrogen export, total phosphorous export, mean buffer width, buffer gap ratio, vegetation vigor and soil erodibility, were all integrated to assess priority. The Soil and Water Assessment Tool (SWAT) model was applied to simulate total nitrogen export and total phosphorous export traveling from upslope areas to the buffer. Other indicators representing the buffer mitigation potential of NPS nutrients were calculated with the help of the Geographic Information System (GIS). To reduce redundancy and objectively discriminate the importance among indicators, a principle component weighting method was used to ascribe weights to each indicator. Total nitrogen export and total phosphorous export, which quantified the NPS nutrients export, were assigned the highest weights of 3.30 and 3.16, respectively. The aggregation result of the multi-criteria analysis generated a distribution map ranking the buffer restoration priorities. The results suggested that restoring the riparian buffers in sub-basins located in the lower reaches of Liuxihe River basin should be a priority. This planning strategy can be applied as a useful supporting tool for water quality management on watershed scale. [Copyright &y& Elsevier]

Abstract: Flood disasters are one of the most common and destructive natural hazards all over the world. In this paper, improved interior-outer-set model (IIOSM) based on information diffusion theory is introduced in detail to assess flood risk in an effort to obtain accurate analytical results that represent the actual situation. Then fuzzy α-cut technique is applied to calculate the fuzzy expected values under the possibility–probability distribution (PPD) calculated by IIOSM. Taking the value of α throughout the interval (0,1], we correspondingly get access to the conservative risk value (R C ) and venture risk value (R V ). Selection of α, R C and R V is dependent on present technical conditions and risk preference of different people. To illustrate the procedure of IIOSM and fuzzy α-cut technique, we employ them respectively to analyze the flood risk in Sanshui District, located in the center of Guangdong province in China. The results, such as risk value estimations, as well as fuzzy expected values, i.e. R C and R V under the given α-cut level, can reflect the flood risk quite accurately. The outcomes of this research based on IIOSM and fuzzy α-cut technique offer new insights to carry out an efficient way for various flood protection strategies. [Copyright &y& Elsevier]

Abstract: Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology. [Copyright &y& Elsevier]

The switch of coal to gas in power generation enterprises is of great significance for tackling air pollution and mitigating climate change. Under China's emissions trading system, power generation enterprises have incentives to switch to low-carbon clean power, and the cost-benefit of switching is the basis of their decision-making. To this end, this paper studied the impact of changes in carbon and fuel prices on switching prices. The results show that: (1) For per yuan increase in the amount of natural gas, coal, and carbon, the switching price increased 4.768, decreased 5.845 and decreased 0.9994 yuan, respectively. (2) The carbon price is relatively low, and the impact of it on switching price is small. (3) The fluctuation in the carbon price, coal price and the natural gas price has a hysteresis effect on switching price. [Display omitted] • This study assesses the coal-to-gas switch possibility in Guangdong Province. • A new mode of operation of coal-to-gas switch is published. • For per yuan increase of carbon price, the switching price decreased 0.99 yuan. • The impact of carbon price fluctuations on switching price has a lag effect. [ABSTRACT FROM AUTHOR]

Farmers are increasingly planting vegetables during the winter fallow period in the double-rice growing area of Guangdong Province, southern China. The intensified winter crop rotation system (rice-rice-vegetable, RRV) may improve the annual planting benefit and change the soil fertility. To address this, yields, biomass, and profitability of each crop in the RRV, relative to the traditional rice-rice-fallow system (RRF), were compared, and the dynamics of physical, chemical and biological properties of soil at different crop stages were determined in this paper. The annual crop yield, biomass and economic return of RRV were significantly higher than those of RRF due to the inclusion of winter vegetables (green beans) in the cropping system. The contribution of green beans to the total revenue per year for RRV was as high as 73%. Filling winter fallow with green beans had no influence on the yield of early and late rice. Soil NO 3 −-N, available P, exchangeable K, exchangeable Fe, exchangeable Cu and acid phosphatase activity at the winter stage were significantly higher in RRV than in RRF, whereas soil pH, soil organic carbon (SOC), dissolved organic carbon (DOC), exchangeable Ca, exchangeable Mg, catalase activity, dehydrogenase activity and invertase activity at the winter stage were lower. The soil NO 3 −-N, exchangeable Fe, exchangeable Cu, and acid phosphatase activity during the early rice stage also tended to be higher in the RRV system. The annual dynamics of soil physical, chemical and biological properties were proven to be greater in the RRV system than in the RRF system. The findings of our study contribute to the understanding of dynamic changes in physical, chemical and biological properties of soil across different crop stages due to intensified green bean planting during the winter fallow period in a double-rice cropping system, which will be useful for the sustainable development of RRV. • Yield and economic income increased significantly in RRV system than RRF system. • Most soil nutrients content were significantly higher in RRV than in RRF. • Soil pH, SOC, and some soil enzyme activity were lower in RRV than in RRF. • The annual dynamics of soil properties were proved to be greater in RRV than in RRF. [ABSTRACT FROM AUTHOR]

• The day-ahead bidding strategy of electric vehicle (EV) aggregator is explored. • Two typical agent modes between EV users and EV aggregator are introduced. • The market uncertainty is handled by analyzing potential market clearing scenarios. • The Guangdong electricity market is first investigated for case study. The electric vehicle (EV) aggregator is a new entity that coordinates the charging of an EV fleet to exploit the business opportunities in the electricity market. This paper explores the optimal bidding strategy for the EV aggregator participating in day-ahead markets. Two typical agent modes between the EV aggregator and the EV user are introduced based on the reality need of the EV user, viz. the centralized protocol management mode (CPMM) and the decentralized demand response mode (DDRM). After that, the stochastic optimization model is developed to maximize the expected profits of EV aggregator. The uncertainties from the electricity market and the EV charging are incorporated into the stochastic optimization model. The Guangdong electricity market, as the pioneer of China's power reform, is first investigated as the target market to verify the rationality of the proposed approach. The possible real-time clearing scenarios are extracted from the historical market clearing data using the multi-class support vector machine (MSVM). Results reveal that the EV aggregator can make an optimal bidding strategy for the potential clearing scenarios when multiple agent modes are involved. Moreover, the comparison of two agent modes indicates that the EVs with the CPMM are more sensitive to the electricity price than those with the DDRM. [ABSTRACT FROM AUTHOR]

Compared with traditional deterministic load forecasting, probabilistic load forecasting (PLF) help us understand the potential risks in the power system operation by providing more information about future uncertainties of the loads. Quantile forecasting, as a kind of non-parametric probabilistic forecasting method, has been well developed and widely used in PLF. However, the results of quantile forecasts are discrete, which contain fewer details than density forecasts which provide the most comprehensive information. This paper proposes a novel day-ahead load probability density forecasting method by transforming and combining multiple quantile forecasts. The proposed method includes two main steps: transformation and combination. In the first step, the kernel density estimation method is used to transform the individual quantile forecast into the probability density curve; in the second step, an optimization problem is established to obtain the weighted combination of different probability density forecasts. The perturbation search method is applied to determine the optimal weight of each individual forecast. We demonstrate the effectiveness and superiority of our proposed method using comprehensive case studies on the real-world load data from Guangdong province in China, ISO New England (ISO-NE) in the US and Irish smart meter data. Case studies show that the combined model is robust to kernel function selection in the transformation step and has better forecasting performance. Compared with the best individual model, the purposed combined model has an accuracy improvement of 1.54% in the Guangdong dataset and 2.9% in the ISO-NE dataset in terms of the continuous ranked probability score. The proposed combination forecasting method can be robust in high volatility scenarios. • Propose a method to obtain combined density forecast by multiple quantile forecasts. • Design the optimal weight determination approach to combine multiple models. • Explore the perturbation search to achieve optimal weight efficiently. • Verify the influence of different kernel functions in the kernel density estimation. • Conduct case studies on three real-world data sets. [ABSTRACT FROM AUTHOR]

• We investigate the integration cost of wind and solar on both the power supply and demand sides. • The integration costs from both sides are increasing with the penetration of variable renewable electricity. • Consumers will bear more cost of variable renewable electricity integration than power system. • Solar power has a lower integration cost than wind power. • Blindly reducing the renewable energy curtailment rate may not be cost-effective. Variable renewable electricity (VRE) is expected to play an important role in global decarbonization. However, due to VRE's intermittency and uncertainty, integrating VRE into the power system may cause additional integration costs for both power systems and consumers. Estimating these integration costs can provide insights for power system planning, support policy making and power market designs. In this paper, the integration costs of wind and solar on both demand and supply sides are quantitatively investigated by an economic power dispatch model combined with Monte Carlo simulation. A case study of Guangdong Province in China is conducted under three scenarios of power mix changes (only increasing the VRE installation; replacing the existing coal-fired power; and replacing the existing coal-fired power without power storage). The results show that integration costs from both supply and demand sides are non-negligible when VRE replaces existing capacity, and the costs are increasing with the penetration rate of VRE. On supply side, it ranges from −2.3 to 12.1 $/MWh for wind penetration from 5% to 30%, and −5.5 to 7.1 $/MWh for that of solar penetration. Moreover, consumers on demand side will bear −7.3 to 185.9 $/MWh integration costs for wind and solar integration, which is much higher than the supply side. In addition, solar power has a lower integration cost than wind power due to a more consistent power output with the load curve of power demand on the time scale. Moreover, our results reveal that blindly reducing the renewable energy curtailment rate may not be cost-effective. [ABSTRACT FROM AUTHOR]

The escalation of risks associated with environmental and climatic aspects has accelerated the introduction of voluntary personal carbon trading (PCT), a policy instrument for promotion and facilitation of low-carbon behavior in the domestic sector. This paper examines participants' motivation in the Guangdong pilot project, which aims to develop the first voluntary PCT scheme (Carbon Generalized System of Preferences, CGSP) in China to reduce the domestic sector's emissions and increase the liquidity of the Guangdong pilot emission trading scheme. The CGSP acceptance model is based on the technology acceptance model and explores the drivers of the public's participation willingness (PW) in the CGSP scheme. Further, the structural equation model has been adopted to explore the influence paths of each driver by using survey data (N = 1190) collected from Guangzhou, Guangdong Province, China. The empirical results demonstrated that the strong and direct drivers are associated with the institutional technical environment, perceived usefulness and participation risk. However, other drivers affect PW through the mediation effect of perceived usefulness. Notably, implementation cost had no significant effect on PW. • An innovative voluntary personal carbon trading scheme in China was introduced. • A conceptual model was proposed to study the determinants of public acceptance. • Structural equation model was used to study the influence path of each determinant. • Both the direct and indirect paths of each determinant have been analyzed in depth. [ABSTRACT FROM AUTHOR]

Abstract: The occurrence, behaviors and fate of 18 PAHs were investigated in a coking wastewater treatment plant in Songshan coking plant, located in Shaoguan, Guangdong Province of China. It was found that the target compounds occurred widely in raw coking wastewater, treated effluent, sludge and gas samples. In raw coking wastewater, high molecular weight (MW) PAHs were the dominant compounds, while 3–6 ring PAHs predominated in the final effluent. The dominant compounds in gas samples were phenathrene, fluoranthene and pyrene, while they were fluoranthene, pyrene, chrysene and benzo[k]fluoranthene for sludge. The process achieved over 97% removal for all the PAHs, 47–92% of eliminations of these target compounds in liquid phase were achieved in biological stage. Different behaviors of PAHs were observed in the primary tank, anaerobic tank, aerobic tank, hydrolytic tank and coagulation tank units, while heavier and lower ones were mainly removed in anaerobic tank and aerobic tanks, respectively. Regarding the fate of PAHs, calculated fractions of mass losses for low MW PAHs due to transformation and adsorption to sludge accounted for 15–50% and 24–49%, respectively, while the rest was less than 1%. For high MW PAHs, the mass losses were mainly due to adsorption to sludge and separation with tar (contributing 56–76% and 22–39%, respectively), and the removal through transformation was less. [Copyright &y& Elsevier]