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In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

Green total factor productivity is a vital measure of sustainable progress, holding immense implications for attaining both economic growth and environmental preservation in a mutually beneficial manner. Focusing on green total factor productivity, this paper examines its nexus with low-carbon energy technology using the Instrumental Variable Generalized Method of Moments (IV-GMM) model, based on a panel dataset in China during 2005–2018. This paper also examines the asymmetric impact of low-carbon energy technology on green total factor productivity. In addition, the moderating and mediating effects are checked. The main findings are that (1) low-carbon energy technology is a promoting factor for green total factor productivity. (2) Low-carbon energy technology exerts a more prominent marginal impact on green total factor productivity when green total factor productivity is at higher quantile levels. (3) Renewable energy generation not only drives green total factor productivity directly, but also further increases the promotion effect of low-carbon energy technology on green total factor productivity, showing a positive moderating role. (4) Energy structure transition and industrial structure transition are two internal impact mechanisms between low-carbon energy technology and green total factor productivity. These findings suggest that the government can promote green total factor productivity development by developing policies related to low-carbon energy technology and fully releasing the effects of technology on sustainable development. • This paper assesses the level of low-carbon energy technology (LCET) in China. • This paper examines the effect of LCET on green total factor productivity (GTFP). • LCET plays a significant role in enhancing GTFP. • Renewable energy generation positively moderates the nexus between LCET and GTFP. • Energy structure transition and industrial structure transition are two mechanisms. [ABSTRACT FROM AUTHOR]

China and the United States are both major countries in the world in the production of and trade in wind energy products, and they are also important trading partners. Therefore, it is of great practical significance to analyze and evaluate the Sino-US bilateral wind power complementarity for re-examining their respective comparative advantages and re-understanding the benefits that trade liberalization brings to both countries, in an attempt to strengthen the trade relations between China and the United States and enhance trade complementarities in wind energy products under the global circumstances of fierce competition. In this paper, based on the trade data from the UN Comtrade database in 2007-2016, a quantitative analysis was conducted on the complementarity of Sino-US wind energy products, by adopting the quantitative research methods of trade combination degree index (TCD), export similarity index (SI) and trade complementarity index (TCI). The results shows that: i) the total trade volume of wind energy products between China and the United States has increased rapidly, and the trade surplus between China and the United States has been expanding year by year; ii) the overall trade balance of wind energy products trade between the two countries is generally stable, and the dependence of China's wind energy products on the United States is much higher than that of the U.S wind energy products on China; iii) the Sino-US wind energy products between China and the United States are highly competitive in the international market; iv) Sino-US wind energy products have different degrees of complementarity in bilateral trade. Finally, this paper proposed policy implications based on the findings of this study. [ABSTRACT FROM AUTHOR]

In spite of ambitious investments by the Chinese government in renewable energy sources, the country's investment in coal power accelerated in recent years, raising concerns of massive overcapacity and undermining the central policy goal of promoting cleaner energy. In this paper, we ask why this happened, focusing on policies that incentivized excessive entry in the coal power sector and using a simple economic model to illustrate the policies' effects. Using coal-power project approval records from 2013 to 2016, we find the approval rate of coal power was about 3 times higher after approval authority was decentralized, with larger effects in regions producing more coal. We estimate that local coal production accounts for an additional 54GW of approved coal power in 2015 (other things equal), which is about 1/4 of total approved capacity in that year. [ABSTRACT FROM AUTHOR]

Abstract Theses on China's low-carbon economy focus on the driving forces of carbon emissions, energy–carbon efficiency, and the carbon–economy nexus. Meanwhile, comprehensive frameworks that assess China's green economy and also analyze its driving forces are lacking. This paper constructs a green productivity index based on the Global Malmquist–Luenberger productivity index to evaluate the development of the low-carbon economy in China. A panel vector auto-regression model is adopted to further analyze the endogenous interactions and dynamic relationships among aspects of the green economy and its influencing factors. Empirical results show that China has seen a balanced development of green economic growth in 2000–2016, achieved in a series of clear stages. To maintain the sustainable growth of the low-carbon economy in the future, China must rely more on technological progress. This research also makes some important discoveries that have not been found in previous studies: the impact of China's industrial structure and energy consumption structure on the green economy are short term, having a duration of impact of approximately four years. Finally, we provide an assessment of provincial green economy development and describe the corresponding policy implications. Graphical abstract Image Highlights • A comprehensive analysis of China's green economy. • A slack-based Global Malmquist–Luenberger index is adopted to calculate the Green Productivity Index in China. • The stage characteristics of green economic development are identified. • A panel vector autoregressive model is used to analyze the interactions among aspects of the environmental economic system. • The findings that the impact duration of energy structure, industry structure, and urbanization on the green economy is about four years. [ABSTRACT FROM AUTHOR]

In order to assess the viability of wind energy projects in China, this paper takes a real options approach by accounting for flexibility over deployment of the technology in response to a stochastic non-renewable energy cost. The difference between the latter and the R&D-enhanced cost of renewable energy operation may be thought of as a benefit from using wind. On the other hand, the Chinese government provides feed-in tariffs to encourage further adoption of wind, which should be included as a cost. This work, thus, shows that there is substantial options value to China's wind energy program, which is bolstered if the cost of carbon dioxide emissions is internalized. Moreover, by varying the rate of feed-in tariffs, the model may be used to show plausible ranges for the feed-in tariffs, which could be a useful policy insight. Furthermore, the study hypothesizes that with the likelihood of renewable energy to replace non-renewable energy, wind power in China can potentially reduce CO 2 emissions by approximately 6.4% by 2020 comparing to the 2005 level and highlights the significant role of wind power in China's emission targets and energy security. This is a relevant paper, especially given the importance of a sustainable energy transition for China. More generally, using the real options approach from a policy perspective could provide insights about the viability of renewable energy programs and robustness of feed-in tariffs rates. [ABSTRACT FROM AUTHOR]

• The analysis framework of a digital business model of PV fishery is constructed. • Multiple value propositions in the digital business model of PV fishery to create value for stakeholders. • The digital business model of PV fishery improves the operational efficiency of PV power generation. • The digital PV fishery increases the diversification of revenue sources of PV agricultural company. A business model that combines solar photovoltaic and fishery plays a role to expand distributed renewable energy and improve rural economy development. However, the business model of solar photovoltaic fishery is encountering growing difficulties with revenues decreasing due to changes of policy such as withdrawal of photovoltaic subsidies and land control. The spreading of digitization in photovoltaic industry and rural areas provides new opportunities for innovation of traditional solar photovoltaic fishery. This paper focuses on multiple value propositions to discuss a digital business model based of solar photovoltaic fishery on a case with a local Chinese photovoltaic agricultural company. The study results show that the digital business model of solar photovoltaic fishery improves the operational efficiency of solar photovoltaic power generation, economic benefits of aquaculture, and the diversification of revenue sources of solar photovoltaic agricultural company and leasing company. The transformation of business model from traditional solar photovoltaic power generation and fishery business to digital service platforms can not only accelerate expansion of distributed renewable energy and be potential deployed globally but also assist photovoltaic agricultural company to effectively respond to policy change. Finally, this paper proposes that supportive policy may focus on advocating system coupling to expedite the development of distributed renewable energies based on digital technology, and digital photovoltaic combined with rural energy market and financial, which will advance development of renewable energy electrification and promote rural economic growth. [ABSTRACT FROM AUTHOR]

In this paper, an improved hybrid MADM method based on interval Decision Making Trial and Evaluation Laboratory (DEMATEL) and interval regret theory are put forward to evaluate five renewable energy alternatives for Fujian province, China. Firstly, the volatility of the evaluation information and the subjectivity of experts or stakeholders are taken into account. Second, political factors are further considered based on existing research. Third, the influence of the subjective initiative of decision makers on the decision result has been paid attention to. Furthermore, the result of the analysis shows that the extended regret theory method should set different parameters to reflect the preferences of decision makers. The ranking results of renewable energy are different with different parameter settings. For example, when α = 0.1 , δ = 0.1 , the priority of clean energy in Fujian Province is hydropower, followed by wind energy, solar energy, geothermal energy, and biomass energy. Sensitivity analysis indicates that the model possesses good serviceability. Based on considering different parameter settings, the Fujian government should invest, in order of priority, in these renewable energy sources to obtain benefits. And the projects related to these renewable energy sources should be evaluated. [ABSTRACT FROM AUTHOR]

The Chinese government identifies the renewable energy sector as a core strategic industry. Since 2009, China is the country with the highest annual investment into renewable energy, predominantly wind and solar photovoltaic projects. Due to rapid cost decline, industrial transformation, and policy support, the relative share of solar project investment is growing at a disproportionate rate. However, there is no systematic analysis of how efficiently these investments are allocated; or the underlying risk and return characteristics. Based on structured interviews with 69 market participants, this paper identifies severe cash-flow uncertainty, unreliable supply chains and a weak regulatory environment as most prominent risk factors that currently inhibit sustainable and demand-driven market development. Expanding on the interview results, this study discusses the origins of risk in China and how the institutional response to investment risk currently fails to address the core problems. [ABSTRACT FROM AUTHOR]

Renewable energy has a critical role in limiting the greenhouse gas (GHG) emissions. This paper assesses the implication of aligning renewable energy deployment target with national emission reduction target for mitigation cost. The assessment methodology uses Asia-Pacific Integrated Assessment/computable general equilibrium (AIM/CGE) model to determine the mitigation cost in terms of GDP and welfare loss under alternative renewable targets in different climate-constrained scenarios. A range of country-specific emission constraints is taken to address the uncertainties related to global emission pathway and emission entitlement scheme. Comparative results show that China needs to increase its share of non-fossil fuel significantly in the primary energy mix to achieve the stringent emission reduction target compared to India. The mitigation cost in terms of economic and welfare loss can be reduced by increasing the penetration of the renewable energy to achieve the same emission reduction target. The modeling results show that coordinated national climate and renewable energy policies help to achieve the GHG emission reduction target in an efficient and cost-effective manner. [ABSTRACT FROM AUTHOR]

In China, the power sector is currently the largest carbon emitter and the transportation sector is the fastest-growing carbon emitter. This paper proposes a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China's renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future. This study applies the proposed model to Shenzhen City to verify its technical and economic feasibility. Modeling results showed that the total net present value of a photovoltaic power charging station that meets the daily electricity demand of 4500 kWh is $3,579,236 and that the cost of energy of the combined energy system is $0.098/kWh. In addition, the photovoltaic powered electric vehicle model has pollutant reduction potentials of 99.8%, 99.7% and 100% for carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively, compared with a traditional gasoline-fueled car. Sensitivity analysis results indicated that interest rate has a relatively strong influence on COE (Cost of Energy). An increase in the interest rate from 0% to 6% increases COE from $0.027/kWh to $0.097/kWh. This analysis also suggests that carbon pricing promotes renewable energy only when the price of carbon is above $20/t. [ABSTRACT FROM AUTHOR]

Densified Biomass Solid Fuel (DBSF) is a typical solid form of biomass, using agricultural and forestry residues as raw materials. DBSF utilization is considered to be an alternative to fossil energy, like coal in China, associated with a reduction of environmental pollution. China has abundant biomass resources and is suitable to develop DBSF. Until now, a number of policies aimed at fostering DBSF industry have been proliferated by policy makers in China. However, considering the seasonality and instability of biomass resources, these inefficiencies could trigger future scarcities of biomass feedstocks, baffling the resilience of biomass supply chains. Therefore, this review paper focuses on DBSF policies and strategies in China, based on the supply chain framework. We analyzed the current developing situation of DBSF industry in China and developed a framework for policy instruments based on the supply chain steps, which can be used to identify and assess the deficiencies of current DBSF industry policies, and we proposed some suggestions. These findings may inform policy development and identify synergies at different steps in the supply chain to enhance the development of DBSF industry. [ABSTRACT FROM AUTHOR]

In recent years, China has successively issued relevant policies to support the development of distributed renewable energy sources such as dispersed wind power (DWP) and distributed photovoltaic (DPV). The capacity of DPV grows fast, but the progress of DWP deployment is limited. In this paper, the improved levelized cost of electricity (LCOE) model and the internal rate of return (IRR) are employed to evaluate the economics of DWP and DPV for case regions. We find that, under current market regime, the IRR of DWP in eastern and central regions is 8.81%–10.34% for the grid side, and which of DPV is 9.08%–12.8% for the user side. When the power market is restructured, the IRR of DWP for industrial and commercial user side increases to 12.73%–14.86%, higher than that the return of DPV. Policy implications on grid-connection, land expropriation, and financial support, planning and approval procedure are proposed to promote the development of DWP. Overall, this paper covers the research gap on the development of DWP in China with a technical/institutional economics perspective and a comparison with DPV. Furthermore, our efforts make a significant contribution to DWP policy by identifying the institutional factors that hinder the development of DWP. • Explore the technical & institutional factors around DWP deployment in China. • DWP and DPV gain comparable economic returns under current planning regime. • DWP can have better economic returns than DPV with electricity market reform. • Identify institutional factors that hinder DWP and propose policy implications. [ABSTRACT FROM AUTHOR]

There is a global move toward being "carbon neutral". Reducing the use of coal to generate power has become an inevitable choice for many countries when transforming their energy structures. Many countries have proposed phasing out coal. China is a major energy producing and consuming country and intends to reach a carbon peak by 2030 and become carbon neutral by 2060. China has repeatedly emphasized coal reduction, but has not explicitly proposed phasing out coal, due to the influence of local governments, coal-related enterprises, and the public. This paper explores whether China could declare a "coal phase-out", and the possible reasons for doing so, by constructing an evolutionary game model with two correlations. MATLAB was used to simulate the model results to determine the effectiveness of the fractal results of the model, and the entropy method was used to calculate the development level of "coal phase-out" related indicators in China and Germany. The results show that: (1) The government can phase out coal only when coal-related enterprises and the public can benefit from reducing coal production and consumption. In addition, these benefits are needed to ensure stable economic and social development without affecting people's daily lives; (2) The development level of relevant indicators of "coal retreat" in China is lower than that in Germany. Based on these results, it is concluded that it is difficult for China to announce a "coal phase-out" at present. Faced with this reality, China should improve the efficiency of coal use, install carbon capture and storage facilities, vigorously develop renewable energy and reduce the share of coal in the energy system. [ABSTRACT FROM AUTHOR]

Developing energy from renewable sources and modernizing the energy system are critical components of China's efforts to combat climate change. Policymakers and authorities have made significant attempts to bring them. However, one of the major impediments to China's energy revolution is financial limitations, which are inextricably linked to the country's economic growth. The present research paper intends to investigate the relationship between economic growth and sustainable financial development on the use of energy from renewable sources in both the short and long run in the context of China. To achieve this, the researchers have utilized the panel data consisting of 10 years from 2011 to 2020. When compared to cross-sectional and time-series data samples, the panel data model offers many benefits. For starters, the panel data includes information on the passage of time and the cross-sectional area. Another benefit of using panel-data models with a larger degree of freedom is that they provide more stable and reliable estimates across short periods across cross-sections. In the case of the short run, there is a positive relationship between economic and financial development and the use of energy from renewable sources in the context of all of China. While in the case of long-term effects, the results indicate the adverse impact of financial development on the use of energy from renewable sources in the western regions of China. These results were deduced using the causality test Granger proposed to determine the path of the causal relationship and the direction of the relationship between the variables. These results indicated that the relationship between economic and financial development in east China was unidirectional, and the nature of the underlying relationship was causal. Meanwhile, in east and west China, economic development in China as a whole has been unidirectionally increasing energy from renewable sources. Our empirical findings suggest many strategies for promoting the growth of energy from renewable sources. [ABSTRACT FROM AUTHOR]

Green bonds have increasingly been utilized around the world as a source of financing for renewable energy development, designed with compliance requirements and measurable economic returns to investors, while mitigating climate change. However, the efficacy of green bond arranged in the emerging economies for financing renewable energy assets and how the underlying risks are managed have remained to be explored. The paper aims to examine the evolving green financial system sponsored by both public and private institutions in managing such risks within China's emerging economy. A case study of green financing for a bundle of wind power assets led by a state-owned enterprise (SOE) reveals an alternative approach by structuring public–private collaboration while stipulating market-based financial incentives to institutional stakeholders under a political economy. This institutional consortium is composed of a state development bank, a commercial bank, credit rating agencies, institutional and private investors, regional power purchasers, and carbon trading entities. Financial stakeholders' risk in such emerging sustainable investment is moderated by these participating institutions and structured "upsides" from carbon trading aligned with the framework of green finance and standards for green bond development. The results reveal the potentials of scaling up the development of renewable energy by adequately managing and sharing key risks, while allocating substantial funding into renewable energy projects under such a green financial system that is to be complementary with a scalable post COVID-19 economic recovery. [ABSTRACT FROM AUTHOR]

• A modified dynamic recursive CGE model is constructed. • Renewable energy growth is considerable only if ETS revenue is used. • Broader renewable energy subsidies are better than ones with narrow scope. • A small part of ETS revenue should be used to subsidize residents for social stability. Emission Trading Scheme (ETS) and renewable energy generation are emission reduction methods in most countries in the world. However, few studies have focused on the impact of ETS on renewable energy. The question is, can carbon trading promote renewable energy generation? This paper first analyzes different distribution strategies of ETS revenue by applying dynamic recursive computable general equilibrium model with multi-sectors. Practical scenarios and better options of distribution of ETS revenue by a comprehensive evaluation based on entropy weight method are proposed. The results show that ETS with no subsidy to renewable will reduce the demand for energy, increase the cost of renewable energy sources and decrease the generation. ETS will be the spring of renewable energy generation when most of the revenue is used for all kinds of renewable energy sources, instead of some of them. The growth of renewable energy generation is also substantial. It is necessary that a small portion of ETS revenue should be used to subsidize residents to reduce the gap between the rich and the poor. If this income is used for government investment and consumption, it will also help to mitigate economic losses, which is caused by the direction of investment by the Chinese government. [ABSTRACT FROM AUTHOR]

• China's Nationally Determined Contributions do not comply with the Paris Agreement. • Overall coal usage needs to decrease by 60% (2 °C) or even 100% (1.5 °C) by 2050. • 100% Renewable energies are cost-efficient to decarbonize the electricity sector. • Sector coupling increases the electricity demand by 100% (2 °C) to 400% (1.5 °C). • Incentives for local actors are needed for a sustainable low-carbon transformation. Growing prosperity among its population and an inherent increasing demand for energy complicate China's target of combating climate change, while maintaining its economic growth. This paper, therefore, describes three potential decarbonization pathways to analyze different effects for the electricity, transport, heating, and industrial sectors until 2050. Using an enhanced version of the multi-sectoral, open-source Global Energy System Model, enables us to assess the impact of different CO 2 budgets on the upcoming energy system transformation. A detailed provincial resolution allows for the implementation of regional characteristics and disparities within China. Conclusively, we complement the model-based analysis with a quantitative assessment of current barriers for the needed transformation. Results indicate that overall energy system CO 2 emissions and in particular coal usage have to be reduced drastically to meet (inter-) national climate targets. Specifically, coal consumption has to decrease by around 60% in 2050 compared to 2015. The current Nationally Determined Contributions proposed by the Chinese government of peaking emissions in 2030 are, therefore, not sufficient to comply with a global CO 2 budget in line with the Paris Agreement. Renewable energies, in particular photovoltaics and onshore wind, profit from decreasing costs and can provide a more sustainable and cheaper energy source. Furthermore, increased stakeholder interactions and incentives are needed to mitigate the resistance of local actors against a low-carbon transformation. [ABSTRACT FROM AUTHOR]

Biomass power is one of the most important renewable energy sources in China. In order to provide a reference for China's biomass power planning, this paper builds a power sector-planning model using the Long-range Energy Alternatives Planning System (LEAP). The results show that in the base scenario, the installed capacity of agricultural and forestry residues, municipal solid waste and biogas will increase to 22350 MW, 21150 MW, and 4900 MW, respectively by 2030. From the point of view of total volume, biomass supply is not a constraining factor for biomass power source. However, there are some social and economic factors that impede the development of the biomass power industry, some of which may not be addressed in the short term. Therefore, the development of the biomass power industry in China is a long-term process. Some policy suggestions were proposed, including reasonable planning and more subsidies for biomass supply value chain. • An optimal power-planning model focused on bio-energy in China was developed. • Agricultural and forestry power, waste power and biogas power were all analyzed. • Social and economic factors that impede biomass power were investigated. [ABSTRACT FROM AUTHOR]