Energy & Environment

Kartal, Mustafa Tevfik

doi: 10.1177/0958305x241300421pmid: N/A

Considering data conversion practices in empirical research, this research investigates the role of data conversion on prediction results in the United States (USA), where yearly and monthly data on energy-related carbon dioxide (CO2) emissions and source-based energy consumption is available, which makes the USA an appropriate case for empirical analysis. In this context, this study considers CO2 emissions as the dependent variable, uses source-based energy use indicators as the explanatory drivers, and performs cointegration regression (CR) approaches on monthly datasets between 1989/1 and 2023/12, which consist of monthly original series (MOS), monthly converted series by quadratic-average-approach (MCSQA), and monthly converted series by quadratic-average-sum (MCSQS). The empirical results reveal that (i) data conversion increases R2 values and improves the goodness of fit criteria of the prediction models, where training and testing results are above 96%; (ii) data conversion causes a change in the coefficients of the explanatory variables. While the direction of the variables changes from MOS to MCSQA and MCSQS, it is the same across between MCSQA and MCSQS, but coefficients and p-values slightly differentiate; (iii) dynamic OLS approach has the highest prediction performance among approaches applied; (iv) the importance of source-based energy use indicators on CO2 emissions differentiate. Overall, the study empirically demonstrates the increasing but varying impact of data conversion on prediction results. Accordingly, the study discusses to benefit of the use of converted data series in empirical predictions, where policymakers can benefit from increasing the impact of data conversion on prediction capacity and prevent incorrect prediction results.

Su, Chi-Wei; Ding, Yu-Mei; Wang, Kai-Hua; Wang, Xiao-Qing

doi: 10.1177/0958305x241300486pmid: N/A

The paper illustrates the dynamic causal connection between green finance (GF) and carbon emissions (CO2) from global perspective. The study indicates that the correlation between GF and CO2 is not uniform in different periods. GF has a dual effect on CO2, the negative effect may be caused by technological innovation, and the positive effect may be explained by the lower prices of traditional energy. Meanwhile, CO2 exerts a beneficial effect on the GF as a result of environmental pressures such as climate change, whereas it has a visible negative impact on the GF owing to factors such as imperfect green financial markets. By analyzing the three paths of limiting highly polluting enterprises, technological innovation and policy signals, the purpose of this study is to develop a theoretical framework in order to demonstrate the significance of the variables. In addition, this paper explains the reasons for the different correlations between GF and CO2 over time in a global view. Countries around the globe can take different measures according to the different causes, which will be of great benefit in reducing global carbon emissions. In this article, it further makes relevant proposals such as improving the system of green financial market, which can be of vital importance to governments, enterprises, and others.

Wang, Yongpei; Li, Keyun; Peng, Liang

doi: 10.1177/0958305x241296447pmid: N/A

Renewable energy is fundamental to achieving China's peak carbon and carbon neutrality targets. Despite the extremely rapid growth in the share of renewable energy generation in terms of provincial-level regions, the impact of renewable energy generation on carbon emission reduction and whether this association is spatially correlated have not been fully recognized. To address this issue, this paper explores the spatial impact effect of renewable energy applications on carbon emissions using Chinese provincial panel data from 1990 to 2020 and a multi-panel spatial econometric model. The results show that the contribution of emission reduction mainly comes from the provincial clean energy mix, while the exclusion of out-of-province power plants also helps to curb their carbon emissions. In addition, in the context of large-scale penetration of renewable energy, grid infrastructure for cross-provincial power trading will contribute to cross-provincial emission reduction. Regulators should take measures to promote coordination of emission reduction policies among provinces in a unified national carbon market, rather than simplifying the allocation of responsibilities in provincial markets. Based on the findings of this paper, there is a need to rationalize and optimize the geospatial distribution of renewable energy projects in the future, so as to maximize the potential of renewable energy to contribute to carbon emission reduction.

Lee, Wen Hui; Husaini, Dzul Hadzwan; Lean, Hooi Hooi

doi: 10.1177/0958305x241293729pmid: N/A

While renewable energy deployment is essential to mitigate climate change, the interplay between renewable energy consumption and environmental degradation may not be linear. The environmental aspect of renewable energy consumption may change over time, depending on the scale and technique effects. This may be due to asymmetry in the relationship. Nonetheless, most current literature either assumes linearity, or ignores the turning point of the behavioral change. This results in inconclusive empirical findings at the disaggregated level of renewable energy consumption. This paper utilizes threshold estimation technique to capture the asymmetry in the renewable energy-CO2 emissions relation in the top ten renewable energy consumers covering the period 1990–2020. The literature gap is addressed by deriving the threshold effect at the aggregate and disaggregated levels to prevent aggregation bias. Understanding the thresholds of different renewable energy sources would improve policy effectiveness and resource allocation at different consumption levels to better curb climate change. The threshold estimation technique measures total renewables, hydro, solar, wind, and others (bioenergy and geothermal) as threshold variables. The findings indicate that total renewables and solar consumptions have stronger mitigating effects on CO2 emissions beyond the consumption levels of 4363.37 and 43.58 kWh, respectively. The advantageous environmental effect of wind consumption only manifests above the consumption level of 657.40 kWh. For policy implication, this study recommends an increase in the weightage of renewables in the energy mix by formulating energy-specific policies, in order to optimize the environmental benefits of renewable energy adoption.

Adeshida, Joel Richmond; Abdalla, Ahmed N.; Furukawa, Noritoshi; Oikawa, Hitoshi

doi: 10.1177/0958305x241293728pmid: N/A

Plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in modern transportation systems, as a result of the ecological crisis and the energy crisis. The enormous potential of the storage capacity of the large fleet of PHEVs, together with the flexibility they can offer power systems through effective control, cannot be ignored. Meanwhile, renewable energy sources (RESs) present significant challenges for energy management scheduling. This article analyzes a microgrid comprising several RESs, such as photovoltaics, fuel cells, wind turbines, microturbines, a battery as energy storage, and PHEVs. Monte Carlo simulation is used to address uncertainties in model development. To account for possible inaccuracies in PHEV charging predictions, electrical load consumption, hourly energy price fluctuations, and RES power output, the study uses a 24-h simulation. Furthermore, a nickel metal hydride battery is used in the microgrid to evaluate the operational impact of different storage systems. The optimization aims to minimize the total cost of the network, including the cost of load supply, the cost of PHEV charging demands, and the cost of power losses. The complexity of the problem requires a novel optimization technique, in this case, the modified manta ray foraging optimization algorithm, which provides a comprehensive global search of the entire search space. Numerical results reveal that the modified manta ray foraging optimization technique demonstrates favorable convergence properties and lower generation costs compared to the classical manta ray foraging optimization method and other recent optimization algorithms.

Gbadegesin, Tosin Kolajo

doi: 10.1177/0958305x241302692pmid: N/A

This study examines the impact of climate change on electricity generation and energy consumption in BRICS and G7 countries and explores how renewable energy adoption can mitigate these impacts. Utilizing energy transition theory, the study provides a robust framework for understanding the shift from fossil fuel-based to renewable energy systems. The paper utilizes the MG ARDL, AUG MG ARDL, and PMG ARDL models to capture the heterogeneous long-run relationships while addressing cross-sectional dependence and heterogeneity. The results indicate that for the G7 countries, higher GHG emissions are associated with an increase in electricity generation and primary energy consumption. In contrast, for BRICS nations, only the PMG ARDL model identifies a positive relationship between rising GHG emissions and electricity generation, while all models indicate that higher emissions are linked to increased energy consumption. Additionally, the results for the G7 demonstrate that a higher share of renewables significantly reduces GHG emissions, a trend not observed in BRICS. The findings highlight the critical role of renewables in mitigating climate impacts and underscore the importance of tailored policy approaches to foster sustainable energy transitions.

Enobong, Sunday Samuel; Abdalla, Ahmed N; Latifi, Mohsen; Suzuki, Kengo

doi: 10.1177/0958305x241296449pmid: N/A

The current state of the power system is significantly influenced by various factors, including price fluctuations, which have an uncertain impact on efficiency. Therefore, employing uncertainty modeling is crucial. This study investigates the application of the Improved Manta Ray Foraging Optimization strategy for intelligently managing electric vehicle parking spots, taking into account the uncertainties associated with changes in primary power grid pricing throughout the demand response program (DRP). By alternating between peak and light-load periods, the proposed strategy effectively reduces daily costs. Key components of the proposed scheme include a non-dominated arrangement model, variable discovery, a memory-based method for selection, and fuzzy logic to identify the optimal Pareto front. The suggested method demonstrates a rapid response time in reaching final solutions and exhibits a high potential for achieving global optima. However, Hydrogen Storage Systems present several significant constraints that must be considered during modeling. The most critical limitations involve the electrolyzer's capacities, the fuel cell boundaries, and the storage tank's capacity. The efficacy of the proposed algorithm is validated within a system that incorporates parking and multiple uncertain resources. Results confirm the exceptional ability of the proposed method to manage uncertainty effectively. Consequently, the cost fluctuations of the system power load have been reduced by up to 41%. Additionally, when DRP is included, the average SPL cost increases by 4.92%, while the variation in SPL expenses decreases by 47.01%.

Jaffar, Haleema; Javed, Nasir; Riaz, Ahmad; Ahmad, Hafiz Abrar; Iqbal, Abdul Mueed; Zhou, Chao; Zhang, Jili

doi: 10.1177/0958305x241300428pmid: N/A

Adaptive transparent system (ATS) is an aspiring and effective building system that can adapt itself through environmental changes in spontaneous and reversible ways. It proposes an automatic and real-time response to indoor and outdoor conditions by increasing building energy efficiency and user comfort. This research presents a critical analysis to provide a preference order of the smart materials being used in the adaptive transparent systems. The approach of using smart material technologies in the building façades is discussed as the promising research direction for the future. A comprehensive literature review is conducted to identify the smart materials used in adaptive transparent systems along with their properties. Seven types of smart materials that are aerogel (AG), phase change material (PCM), photovoltaic (PV), electrochromic (EC), thermo-chromic (TC), thermo-tropic (TT), and liquid crystal polymers (LCP) are selected based on evaluation criteria determined by the literature. The identified criteria are bulk density (BD), thermal conductivity (TCd), sound insolation (SI), fire retardation (FR), heat transfer coefficient (HTC), solar transmittance (ST), air purification (AP), annual energy saving (AES), cost saving (CS), ultraviolet (UV)/near infrared (NIR) control (UNC), and embodied carbon (EC). The identified seven smart materials are investigated with the help of two multi-criteria decision-making (MCDM) techniques named analytical hierarchical process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) for analysis and ranking of materials. Results of the analysis demonstrate that PV is the most optimum material for adaptive transparent systems while others are ranked according to their performance.

Zhang, Yi; Zhao, Zhe; Teng, Zhaoyue; Wang, Jingyu

doi: 10.1177/0958305x241300423pmid: N/A

This article examined the influence of the energy-consuming rights trading system (ERS) on environmental inequality, discussed the influence mechanism through the lenses of industrial structure upgrading and green technology innovation, and analyzed the possible spatial spillover effect of ERS with panel data at the provincial level in China from 2003 to 2021. The results indicate that ERS can significantly control provincial environmental inequality by advancing industrial upgrades and fostering green technology innovation. The controlling effect of ERS on environmental inequality is more obvious in regions with more serious environmental inequality, and the controlling effect on the old industrial base is also greater than on the non-old industrial base. Additionally, ERS has helped control environmental inequality in surrounding provinces. In light of the research findings, a series of policy recommendations have been put forth with the aim of further developing and enhancing China's energy-consuming trading market. Overall, this article presents new empirical evidence of the interplay between the energy consumption trading market and environmental inequality. In addition, it offers new approaches and references for policymakers to improve the energy-consuming trading market and the environmental inequalities.

Mi, Li; Huang, Yongjun; Sohail, Muhammad Tayyab; Hafeez, Muhammad

doi: 10.1177/0958305x241293740pmid: N/A

For the sustainable future of the world, achieving carbon neutrality is crucial. Most of the countries are making fundamental changes in their economic models so that they can become carbon neutral by the year 2050. In this regard, green growth should be the preferred model for most economies in the world. The literature on green growth is growing, but no past study has shed light on how financial deepening, smart urbanization, rural development, and digital economy impact green growth within the framework of carbon neutrality targets. Thus, there exists a significant gap in the literature, which motivates to conduct this analysis. The primary motive of this study is to analyze the asymmetric impact of financial deepening, smart urbanization, rural development, and digital economy on green growth within the framework of carbon neutrality targets. Empirical estimates are obtained by utilizing the nonlinear autoregressive distributed lag framework. The results confirm that positive changes in financial deepening, smart urbanization, ICT, and rural development help promote long-term green growth. The negative changes in financial deepening hinder green growth in the long-run, while the negative changes in smart urbanization, information and communication technology (ICT), and rural development do not significantly impact green growth in the long-run. In the short-run, only the positive changes in financial deepening and ICT promote green growth, while the rest of the estimates are insignificant. The results imply that financial deepening, smart urbanization, ICT, and rural development are vital in achieving carbon-neutrality objectives by fostering green growth. Thus, policymakers should rely on the green growth framework to achieve net zero carbon emissions.