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China's coalbed methane (CBM) industry is in an extremely important "climbing period" and "strategic opportunity period", which can be generally characterized by "three low and one small": low degree of exploration and development, low adaptability of main technology, low return on investment and small development scale. Under the "carbon peak and neutrality" background, the development status of CBM industry is systematically combed. The resources, technology, management problems and reasons are analyzed. Strategies and countermeasures to accelerate the industrial development are put forward according to the factors such as efficient development of resources, major technical breakthrough, talent team training, policy formulation and implementation, return on investment and so on. The existing problems are as follows: (a) The overall occurrence conditions in China are complicated and the development is difficult compared with the other countries. (b) The research precision accuracy of CBM resource conditions is not enough. (c) The adaptability of technology is poor. (d) The management mode is not suitable. In view of these problems, this paper puts forward the "two steps" development strategy and the technical and management countermeasures of "five in one". The corresponding "five in one" technology and management countermeasures are the formulation and implementation of relevant safeguard measures in accordance with the principle of collaborative innovation in five aspects: resources, technology, talents, policies and investment. Through the above measures, the dream and grand blueprint of CBM industry shall be realized. [ABSTRACT FROM AUTHOR]

Using 34 products from China's commodity futures market, this study examines the impact of social network attention and sentiment on its futures market returns. A machine learning text analysis algorithm was used to construct social network investor sentiment in consultation with three search volume indices. We find that: social network sentiment is a good predictor of commodity futures returns, investor attention has a significant positive impact on returns and absolute returns, and the Baidu index is better at forecasting returns than the Sogou and 360 indices. In addition, we examine how social network sentiment affects returns at different levels. We find that extremely high, market social network sentiments of investors changed the predicted results significantly; thus, the bases of the specified trading strategies of investors were altered. Regulators should therefore incorporate investor sentiment into regulatory targets and enhance retail investor education. [ABSTRACT FROM AUTHOR]

The spread of coronavirus disease 2019 (COVID-19) has had a significant impact on business and economies globally. This paper aims to investigate the impact of COVID-19 on firm performance in China's high-tech industries. The population of this paper is 116 companies listed on the sci-tech innovation board (STAR market) of the Shanghai stock exchange. DuPont analysis is applied to measure firm performance including return on equity (ROE), return on assets (ROA), asset turnover ratio (ATO), and profit margin. The empirical results show that the COVID-19 pandemic has a significant and negative impact on ROE, ROA, and ATO in high-tech industries, and research and development (R&D) investment has a moderating role in the relationship between the COVID-19 pandemic and firm performance. The findings may play a guiding role for corporate managers and policymakers to overcome this formidable crisis in the era of COVID-19. [ABSTRACT FROM AUTHOR]

In 2017, the 19th CPC National Congress proposed to "establish a sound economic system of green, low-carbon and circular development", which indicates the direction of high-quality economic development in the new era of China. Clean power investment is a powerful way to promote high-quality economic development by adopting non-fossil-energy utilization and low-emission technologies, as well as creating new jobs. Meanwhile, large-scale investment and a long investment return period result in negative effects on local economies. To better understand the effect of clean power investment, this paper selects panel data of thirty provinces in China from 2010 to 2019 to establish a spatial Durbin model to explore the impact of clean power investment on regional high-quality economic development. The results show that inter-regional high-quality economic development shows significant spatial auto-correlation characteristics. Clean power investment has not only a positive direct effect on high-quality economic development but also generates positive spatial spillover effects. Human capital, degree of government intervention, and urbanization rate have positive effects on regional high-quality economic development, while they play a suppressed role on neighboring regions. [ABSTRACT FROM AUTHOR]

Public capital plays a key role in national economic production in developing countries, and it has become the focus of factor reform in China. This paper aims at studying the dynamic characteristics of China's public capital and the return rate. To this end, this paper inventories the public capital stock from 1978 to 2017; constructs a two-sector model to derive the necessary condition for optimal investment related to the return rate, and empirically tests it; estimates the output shares of the two sectors and calculates the rate of return to capital; and illustrates the dynamic evolution of public capital and the return rate from the time and regional dimensions. The study shows that, first, public capital has grown significantly. The portion of public capital held in the form of state-holding is increasing year by year. However, the proportion of public capital to total capital has decreased from 81% to 35%. Second, the growth of public capital has effectively balanced the gap of capital between regions. Third, the rate of return to capital has been decreasing, with two sectors' return rates having converged in 1981 and in 2010–2013. This paper measures the stock of public capital and its return rate, which is beneficial for formulating the policy of public capital in economic production and distribution, as well as exploring the path to achieving common prosperity. [ABSTRACT FROM AUTHOR]

The research presents the interrelationships between banking performance, competitiveness and sustainability of the three banking systems and the impact of each sustainability variable selected on the other macroeconomic indicators in short, medium and long-time horizon. The empirical study involved the use of a panel Auto Regressive Vector methodology and was based on macroeconomic indicators relevant to the performance of the banking system (return on assets, return on equity, an annual growth rate of Gross Domestic Product), as well as indicators that can be assimilated to sustainability (renewable fuels used, CO2 emissions). The dependent variables were return on assets and the annual growth rate of Gross Domestic Product. The global sample analyzed comprises 29 countries, spread over three continents (Europe, North America, Asia), with data collected over a 10-years period (2011-2020). These countries together account for approximately 62% of global GDP (data from 2020). The research results show that as banks invest in green energy and sustainable products, competitiveness will also increase, which will have a negative impact on profitability in the short term. In the medium and long term, this impact will become positive also in terms of profitability increase. This strategic move to develop sustainable business models and to finance a higher percentage of green investments also adds extra competitive advantages, such as reputation and smart differentiation, from other less sustainability-oriented banking systems. The process impacts the systemic level, the macro perspective, the banking organizational level, the micro perspective, together with the perception of the customers. [ABSTRACT FROM AUTHOR]

A portfolio selection model with return as triangular intuitionistic fuzzy number is developed in this study to assess and select portfolios on China Stock Exchange. Although the portfolio selection has been widely investigated, and most studies have regarded return and risk as the main decisive criteria, there are many uncertainties in the financial market, such as social, political and human psychological factors, which makes it difficult for us to describe the risks in line with empirical evidence. To fill this gap, first, a literature review was conducted to clarify the current situation and shortcomings of portfolio selection. Second, the triangular intuitionistic fuzzy number was used to fuzzify the coefficients of the objective function and the constraints in the portfolio model. Third, the triangular intuitionistic fuzzy number model was transformed into a linear programming model by using the selected exact ranking function, and the model was solved by MATLAB. Finally, the historical monthly returns of 10 stocks in China's Stock Exchange from December 2017 to November 2020, lasting 36 months, were selected to demonstrate the model. The results indicate that the portfolio selection model with triangular intuitionistic fuzzy number can better meet the uncertainty of the real securities market, and more reasonable investment decision guidance can be provided for investors. Besides, the limitations of this study are pointed out, and implications and directions for future research are discussed. [ABSTRACT FROM AUTHOR]

Biofortification is a new agricultural intervention to alleviate hidden hunger in China and even the world. Exploring the impact of farmers′ adoption of biofortification on return on investment (ROI), which is calculated from farmers′ production net income and profitability, can provide empirical support for the development of sustainable agriculture. This paper examines the impact of the adoption of biofortification on ROI using cross-sectional data from a survey of farmers in China. An endogenous switching regression model that accounts for selection bias was used in the research. The empirical results revealed that the adoption of biofortification has a positive and statistically significant impact on ROI. A heterogeneity analysis also revealed that comparatively high annual income adopters and female adopters tend to benefit more from the adoption of biofortification than middle- and low-income adopters and male adopters. [ABSTRACT FROM AUTHOR]

Chinese overcapacity in the steel and coal industry has been on the rise since 2013, which leads to the misallocation of resources and decreases in production efficiency. In 2015, the Chinese central government adopted a series of de-capacity policies to resolve excess capacity and improve corporate profitability. However, there is scant evidence on the impacts of de-capacity policies on the firm profitability. Based on the data from Chinese listed companies in the steel and coal industry, this study constructs the difference-in-difference (DID) method to investigate the effects of the de-capacity policy on the profitability of listed companies in the steel and coal industry empirically. The results show that the de-capacity policy significantly increases the return on equity (ROE) of the experimental group, which is higher than that of the control group by 12.4%. That is partially because of the improvement in gross profit margin, management efficiency, and return on manpower due to the de-capacity policy. This study offers new evidence on the efficiency of China's de-capacity policy toward the steel and coal industries through data at the enterprise level. [ABSTRACT FROM AUTHOR]

The number of wastewater treatment plants (WWTPs) in China is fast growing as the country's urbanization accelerates. WWTPs, part of the high-energy-consumption industry, must use a lot of energy in wastewater treatment. PV projects in WWTPs are viable solutions for energy conservation, but PV project investors, WWTP owners, and government authorities need to conduct rigorous economic and ecological assessments. This article examines the PV potential, financial feasibility, energy savings, and emission reduction effect of large-scale WWTPs in China using the cable-supported system. Furthermore, China's total PV power potential for urban WWTPs has been assessed at 5.6 GW for the first time. According to economic feasibility analysis, 26 of the 31 WWTP-PV projects in China's various regions are economically viable. Moreover, the WWTP-PV projects can assist WWTP in reducing carbon emissions by 10%–40%. Further sensitivity analysis reveals that practically all WWTP-PV projects will be viable in the future, assuming that certain solar resource requirements are met, and appropriate investment strategies are implemented. • China has a total PV power potential of 5.6 GW for urban WWTPs. • The economic feasibility analysis shows most WWTP-PV projects in China are viable. • The WWTP-PV project can help WWTP reduce carbon emissions by 10%–40%. • The PV potential of a WWTP is correlated with its planned wastewater treatment capacity. [ABSTRACT FROM AUTHOR]

The selection of suitable locations for rooftop photovoltaic projects (RPVP) is critical for optimizing power generation efficiency and return on investment. However, traditional methods of site selection that rely on subjective assessments of index weights can compromise accuracy, while complex calculations may limit adaptability to changing real-world data. In this study, we proposed a data-driven ensemble learning framework that integrates socio-economic, environmental, climate, and geography factors to optimize RPVP site selection. Using data from 1589 counties in China, we mapped eight criteria to feature variables to facilitate machine learning classification. Furthermore, the K-means algorithm was employed to enhance the model's robustness against outliers. The findings indicate that the proposed stacking model exhibits superior performance in comparison to other classifiers, as evidenced by the higher scores of performance metrics. Specifically, for positive instance prediction, the stacking model achieves the highest Precision scores. According to the rankings of Precision scores derived from the four ensembled models, we categorized counties suitable for RPVP development into five priority tiers. The ensemble learning framework provides a valuable and reusable tool for advancing county-level RPVP site selection and serves as a motivation for selecting other renewable power plant sites. • Ensemble learning is used in site selection of rooftop photovoltaic projects. • Stacking model presents the best performance on minimizing the erroneous selection. • The suitability of power plant siting is divided into several levels. • The proposed site selection tool can adapt to changing real-world environments. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive system for assessing contaminant emissions, including the dominant contaminant reduction effects, recessive contaminant reduction effects, and contaminant reduction efficiency indexes. The system is used to evaluate the linkage changes in contaminant emissions reduction across different sectors, the ease of regulation, and the returns on emissions reduction efforts. The study's results demonstrate that firstly, COVID-19 initially reduced contaminant emissions across almost all sectors, although this effect waned over time. Secondly, applying conventional regulatory measures to sectors with high reductions in contaminant emissions may not yield the expected results. Thirdly, while some sectors may be more difficult to regulate, they may offer a higher return on investment in terms of emissions reduction. Based on the above results, the paper advocates for timely measures to consolidate environmental gains, prevent rebounding of contaminant emissions, prioritize supervision and management of easily regulated sectors, prioritize upgrading of cleaner production in sectors with high reduction efficiency indices, and increase investment in environmental technology. • Created a new comprehensive system for assessing contaminant emission changes. • Quantified the unique linkage changes of contaminant emissions during COVID-19. • Developed new models to reveal contaminants' regulatory difficulty and return. • The innovative reduction effects of four key contaminant indicators were measured. • Applied WHEM firstly in research across multiple areas of contaminant emissions. [ABSTRACT FROM AUTHOR]

As a carbon reduction technology, CCUS (Carbon Capture, Utilization and Storage) is an important way to achieve the goal of "carbon peak and carbon neutrality" in China. In order to change the situation of slow commercialization and large-scale development of CCUS, this paper proposes to establish a CCUS cooperative alliance. Taking alliance organizers, institutional investors, upstream operators and downstream operators as research objects, the evolutionary game model is developed to reveal the dynamic evolution mechanism of CCUS cooperative alliance and analyze the impact of exogenous variables on the strategic choices of alliance members. The results show that: (1) establishing a strong market-oriented alliance can promote the large-scale deployment and commercialization of CCUS, (2) when return on investment is greater than or equal to 6%, it can effectively promote the investment of institutional investors in CCUS field, (3) when the government subsidy rate is greater than or equal to 75%, the willingness of downstream operators to operate independently is significantly enhanced. This study reveals the operation mechanism of the alliance, provides theoretical and methodological support for the operation and management of the alliance, and is of great significance to promote the cooperative operation and development of the alliance. • A CCUS cooperative alliance with commercial characteristics is proposed. • An evolutionary game model is developed to reveal evolution mechanism of alliance. • ROI of 6% or more can promote institutional investors' investment in CCUS. • Subsidy rate of 75% or more can urge downstream operators to operate independently. [ABSTRACT FROM AUTHOR]

Marine ranching have attracted wide attention in the marine fishery in recent years owing to numerous benefits for sustainable development. However, existing research efforts on marine ranching risk management are very limited, and no prior in-depth research has focused on studying the risk interdependencies in marine ranching from the perspectives of multiple risks. This paper begins by identifying 29 marine ranching risks using a systematic literature review and experts propose. Next, the cause-effect diagram and hierarchical network model of marine ranching are obtained using the combined DEMATEL - ISM method, which clearly confirms the interdependent relationships among various risk factors. It also identifies the most critical factors, which enables the operators of the marine ranching to make decisions to reduce risks. Then the hierarchical network model is mapped to a BN and expert judgments are further transformed into the conditional probability distribution by software Netica, in order to quantify the strength of the interdependent relationships among the marine ranching risk system, and determine main paths resulting in risk occurrence. According to the research results, the risk occurrence probability of marine ranching is high, with a probability of 85.1%，and the most approximate cause path leading to the occurrence of risk are determined—namely, X 27 (Long construction cycle) → X 10 (Financing difficulties) → X 15 (Safety hazards) → X 11 (Investment return risk) → Y(Marine ranching risk) and X 9 (Lack of technical talent) → X 8 (Monitoring failure) → X 2 (Habitat destruction) → X 26 (Large preinvestment) → X 10 → X 15 → X 11 (Investment return risk) → Y. Through the established risk analysis model, the key risk factors in the implementation process of marine ranching can be obtained, which is helpful for in-depth understanding of the risk information of marine ranching and more effective risk management of marine ranching. • The risk factors of marine ranching in China are identified. • DEMATEL, ISM and BN are used in the research of risk assessment of marine ranching. • The hierarchical relationship of risk factors of marine ranching is figured out. • The most approximate cause path leading to the occurrence of risk is determined. [ABSTRACT FROM AUTHOR]

In this paper we explore the impact of a feed-in tariff (FIT) reduction in combination with the introduction of a renewable portfolio standard (RPS) on the development of distributed photovoltaic generation (DPVG) in China. This is done using system dynamics models that can simulate a DPVG's return on investment, installed capacity, levelized cost of electricity, and grid parity for multiple scenarios. The results show that the FIT reductions have a short-term negative effect on the DPVG installations. However, they can be performed in China without sacrificing the grid parity goal. In fact, FITs could be completely cancelled within the next 2–3 years. It is more effective to simply cancel the FITs after maintaining the current FITs for several years compared to a slow FIT reduction every year. After the FITs are cancelled, an RPS can effectively help continue the trend towards grid parity. However, we also need to consider the relation between the RPS quota, the installed capacity, and the tradable green certificate-supply/demand/price. The DPVG industry has already realized the demand-side grid parity, while residential- and commercial-DPVG are expected to reach the supply-side grid parity within 4–7 years and 5–8 years, respectively. • Development of a system dynamics model of China's distributed PV generation. • Simulation and cost/investment analysis with different scenarios. • Effect of feed-in tariff reductions and introduction of renewable policy standard. • Grid-parity analysis of residential and commercial distributed PV generation in China. [ABSTRACT FROM AUTHOR]