Energy Strategy Reviews最新文献

This study investigates the feasibility of siting a nuclear power plant (NPP) within a densely populated university campus. Site selection for NPPs is a complex process involving numerous interconnected factors. To address this complexity, we employ system dynamics (SD) modeling, a methodology renowned for its ability to quantify both technological and social aspects. Our simulation results indicate that public acceptance (PA) is a critical determinant in site selection. In the fourth month of the simulation, the model's values were identical in both PA and non-PA scenarios. However, subsequent simulations revealed a significant divergence, with PA-implicated values reaching 85.398 compared to 24.240 in the non-PA scenario. This disparity underscores the primacy of PA in the decision-making process. Given the extended timeline of NPP construction, the gap between PA and non-PA outcomes is likely to widen further, particularly in the context of positive public sentiment toward the NPP. The insights gleaned from this research can be applied to other industries, such as oil refineries, where the potential for atmospheric pollution due to toxic gas leaks necessitates careful site selection.

I assessed energy efficiency performance and investigated transition tendencies for economies at different development levels. I found that energy efficiency performance is tied to the level of economic development, with developed economies exhibiting higher performance than developing economies. Furthermore, developed economies are more likely than developing countries to transition out of a low energy-efficient (LEE) state. Consequently, achieving higher energy efficiency (HEE) status is expected to be highly sustainable in the short-, medium-, and long-term for developed countries. However, for similar achievements, I found moderate sustainability in the medium-to long-term for upper-middle-income countries and higher unsustainability in the medium-to long-term for lower-middle-income countries. Addressing the gap in the global energy efficiency system requires a ‘big push’ investment in energy efficiency, particularly in developing countries, in addition to implementing a broad policy overhaul aimed at eliminating or reducing market barriers and inefficiencies in energy efficiency.

Boosting the core competitiveness and operational efficiency of enterprises while minimizing costs through strategic R&D investments is crucial for the advancement of wind power companies. This paper examines data from listed wind power enterprises from 2015 to 2023 and develops a detailed multiple linear regression model for comprehensive analysis. Goldwind Science & Technology Co., Ltd. is used as a case study. The paper explores the impact of R&D investment on wind power enterprise performance through empirical and case analyses, revealing several key insights. First, both R&D intensity and per capita R&D investment positively affect wind power enterprise performance, with R&D intensity having a more substantial impact. Second, the case study of Goldwind demonstrates a clear positive correlation between R&D intensity, per capita R&D investment, and corporate performance. Third, the impact mechanism analysis shows that increased R&D investment enhances Goldwind's technological advancements, leading to new products that meet market demand and improve enterprise performance. Based on these findings, the paper proposes policy recommendations, including increasing R&D investment and recruiting more scientific researchers.

Carbon taxes and emissions trading systems (ETSs) are the two most popular policy tools in the global effort to achieve net-zero carbon emissions. Herein, we review empirical and theoretical research on carbon taxes and ETSs, focusing on the policies’ design, implementation, and effectiveness in various environmental and economic contexts. We provide a comprehensive comparison of the relative advantages and disadvantages of these two policies and offer insights into their future design and application. Our main conclusion is that an integrated mechanism that combines features of both policies with those of other complementary policies may be a more effective approach to emissions abatement than solitarily using either policy.

Industry-university-research collaboration is the primary channel of industrial innovation, and China is a common context for research on innovation in the new energy vehicle industry. This study analyzes the industry-university-research collaborative innovation network in China's new energy vehicle industry using various software programs, including Gephi and ArcGIS, based on collaborative invention patent data related to the new energy vehicle industry provided by the global patent database incoPat. According to the findings of this study, the highest number of patents obtained through industry-university-research collaboration was observed in new energy vehicle device and accessory manufacturing, whereas the number of new entrants was significantly higher than the number of incumbents albeit in a downward trend, suggesting that the creation of new collaborative relationships among new entrants has always been the main phenomenon in the process of building an industry-university-research innovation system in the new energy vehicle industry. As private firms have long been playing an integral role in industry-university-research collaboration in the new energy vehicle industry, the number of private firms in the network exhibited an upward trend annually, while the dual heterogeneous connection mode was the primary connection mode in industry-university-research collaboration. There were some differences between the spatial characteristics of intraprovincial and interprovincial collaboration in the industry-university-research collaborative network in China's new energy vehicle industry. The analysis results obtained using GeoDetector revealed that the influence of economic base, level of openness to the outside world, government dominance, market dominance, regional science and technology finance, and entrepreneurial spirit varied from one stage to another.

The investigation of the impact of energy consumption patterns on peak emissions in China is pertinent and crucial for achieving global climate goals. The study aims to form a forecast of carbon emissions in China and diagnose the achievement of established decarbonization goals based on structural changes in primary energy consumption until 2030. To achieve the goal, the study formed a Dynamic stochastic general equilibrium (DSGE) model with the implementation of Markov chains. The obtained research results yield important insights into the influence of various scenarios on carbon emissions in China by the year 2030. Specifically, considering structural transformations, an increase in emissions by 15 % can be anticipated, which is 7 % less than predicted by the classical scenario. Among the proposed scenarios, the Net Zero scenario is deemed the most efficient, where emissions could reach a level of 8509.5 million tons of CO₂. Conversely, the New Momentum scenario proves to be the most precarious, as it entails an escalation in coal usage, leading to a 4 % increase in emissions, amounting to 13264.84 million tons of CO₂. The contribution of this study is the approach to conducting carbon emission projections, which is based on the DSGE model with the implementation of Markov chains. This makes it possible to assess the adequacy of the set targets for carbon reduction based on alternative scenarios, which in crisis conditions can overlap and thus complement each other.

In energy system transformation studies, the assumption of linearly rising CO₂ prices is common and based on the decreasing issued European allowances (EUA). The actual auction price for allowances is influenced by market dynamics, resulting in non-linear variations in CO₂ prices due to the possibility of banking allowances. This study explores the impact of non-linear CO₂ price trajectories on the European electricity market. Various scenarios, such as increasing prices with the market interest rate, early price peaks, collapse, or fluctuations, are examined until 2030. The study quantifies the effects on power plant investments, profitability, consumer costs, and government revenues. The findings reveal that early and high price signals lead to an earlier transition with 7 % more wind power plants but at 4 % higher costs. Conversely, lower initial prices delay power plant investments in wind power plants by 6 % and result in 21 % higher emissions. Compensation for climate impact costs can yield a positive cost-benefit analysis in scenarios with early and high price signals when emissions are avoided through mechanisms like the Market Stability Reserve (MSR).

Asia, the world's highest emitting region with surging electricity demand, must transition to clean energy sources for sustainable development. Despite significant progress in solar power deployment, its penetration in the electricity mix remains relatively low. This study employs process tracing to compare solar power development in China and Thailand from 2000 to 2023, representing state-driven and society-centered governance approaches. By analyzing data from secondary sources and conducting online stakeholder interviews, we assess the role of energy governance in fostering resilience during the low-carbon transition. We find state-driven governance exhibits resilience in addressing immediate crises, while society-centered governance drives societal transformation towards a low-carbon future. While state-centric governance suits large-scale centralized renewables, society-centered governance is crucial for distributed renewable projects. Path-dependent governance determines how the energy sector takes in niche innovation, and the reinforced governance structure constrains energy resilience given the interaction within and of the sector with other societal actors. The findings provide insights to inform evidence-based policies and interventions for renewable deployment.

The composition of energy consumption is shifting from fossil fuels to renewable energy sources. Determining if reducing energy use may increase green factor production (GFP) is thus crucial. This study, which examines data from 2013 to 2019, indicates that GFP is impacted by both the Robotics and natural resource markets. It conducts a comprehensive analysis of Robotics's impact on the development of green economies using China as a case study and a suitable mathematical model, namely a static panel model (SPM). By applying robotics analytical powers and predictive modelling, the research sheds light on how economic growth and environmental preservation might be balanced, or “green growth.” The magazine aims to find synergies that support the incorporation of AI-driven solutions into policy frameworks, promoting a shift toward more environmentally sustainable practices globally by carefully analyzing global economic policies. It has been observed that different industries and development phases have other impacts of AI on carbon intensity. Furthermore, this investigation dissects GFP into its component elements, green efficiency improvement effects, and technological advancement to elucidate how AI influences green economic growth.

In densely populated countries, land competition is a key challenge in light of a growing population and the land-intensive decarbonization of energy supply. We apply an energy system model using linear optimization to Germany as an example for a densely populated and industrialized nation with a high energy demand to show how land competition affects the economics of land-intensive renewable energies. Bioenergy crops are currently cultivated on 6.5% of Germany’s land area. We find that allocating only 6% of the total land to the future energy system, which is even less than the current allocation to bioenergy crops, allows for a system that is close to the cost-minimum that we calculate when not restricting the land area. This 6% of the land area is divided into 4% for photovoltaics (PV), 2% for onshore wind and 0% for bioenergy crops. This would save 15 billion €/a (15.1%) relative to the system that matches current political targets for utility-scale PV. For areas exceeding this 6%, we find that the most cost-efficient utilization comes from bioenergy crops, but they only add value to the energy system if there is plenty of land available. The value of land to the energy system is at least twice as high for 0% remaining emissions when compared to the case of 10% remaining green house gas emissions, although both scenarios are separated by less than five years according to current German law. Both our findings underline that considering the value of land as early as possible is necessary when developing state policies that shall lead to cost-efficient renewable energy systems.