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Energy economics
Elsevier Science
Energy economics

Elsevier Science

0140-9883

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    Substitution effects of high-speed railway on carbon mitigation: From theory to empirics

    Wang, JianDu, HuanhuanLin, Kefu
    1.1-1.15页
    查看更多>>摘要：Understanding the substitution effects of public transit developments, such as the introduction of high-speed railway, is critical for advancing carbon abatement and combating global climate change. This study aims to analyze the profound impacts of high-speed railway, specifically the Kyushu Shinkansen in Japan, on the sustainability performance of passenger vehicles. We first build a theoretical model to reveal the underlying mechanisms of substitution effects. Consistent with theoretical predictions, we employ a difference-in-differences approach to examine the causal impact of the high-speed railway rollout on carbon emissions from passenger vehicles in the Kyushu region of Japan. Our findings reveal a notable decrease in carbon emissions per capita from passenger vehicles in regions served by the high-speed railway, compared to those non-serviced areas. Meanwhile, we confirm that these substitution effects are less pronounced in the freight transport sector. This paper not only contributes to the understanding of HSR's energy impact but also provides valuable insights into the broader implications of transportation infrastructure development on sustainable transportation practices.
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    Cost-effective intelligent building: Energy management system using machine learning and multi-criteria decision support

    Cai, HelenZhang, WanhaoYuan, QiongSalameh, Anas A....
    1.1-1.15页
    查看更多>>摘要：Enhancing cost-effective energy management in buildings is critical for achieving sustainability goals and addressing the challenges posed by rising energy use, which is a major concern for energy policy frameworks worldwide. This study is a trailblazer in using multi-criteria decision-making (MCDM) methodologies for the real-time operational optimisation of building energy systems. Data collection and pre-processing, feature extraction, feature selection, classification, trust authentication, encryption, and decryption are among the techniques used in this approach. Pre-processing procedures for the raw data include feature encoding, dimension reduction, and normalisation approaches. The Hybrid Grey Level Co-occurrence Matrix Fast Fourier Transform (HGLCM-FFT) method is used for feature extraction. Filter-based methods are used for feature selection, including IG, CS, symmetric uncertainty, and gain ratio. The Hierarchical Gradient Boosted Isolation Forest (HGB-IF) technique is used for the classification. Distributed Adaptive Trust-Based Authentication (DATBA), a security architecture in distributed cloud environments, uses trust authentication. The Particle Swarm Optimized Symmetrical Blowfish (PSOSB) method is used for encryption and decryption.The proposed framework not only ensures robust data security but also provides actionable insights for energy efficiency improvements, aligning with broader economic and environmental objectives. The suggested work is implemented using OS Python - 3.9.6; the performance of the proposed model is Attack Detection Rate, False alarm rate, True positive rate, Network usage, CPU usage, Encryption time, encryption time, and Throughput.
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    Winners and losers of the EU carbon border adjustment mechanism. An intra-EU issue?

    Amendola, Marco
    1.1-1.24页
    查看更多>>摘要：The paper develops a Multi-Regional Input-Output analytical framework to study the EU's recently adopted carbon border adjustment mechanism (CBAM). This policy introduces carbon tariffs to replace free allowances in several Emission Trading System (ETS) industries to reinforce and extend the EU carbon price signal while mitigating the risk of carbon leakage. Yet, the policy has prompted immediate international equity concerns, particularly regarding its potential burden-shifting effect, especially on low-income countries. In this context, the analysis provides a comprehensive examination of the distributional impacts of the EU CBAM, shedding light on the countries and industries most affected by the policy. Contrary to the apprehensions, the findings indicate limited evidence of burden shifting, with such a phenomenon being confined to a few specific geographical areas and industries. Instead, the results unveil more pronounced redistributive impacts within the EU, with certain Eastern EU countries facing particular losses from replacing free allowances with CBAM. Adverse competitiveness effects and carbon leakage in various downstream industries are also identified.
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    Navigating renewable technological innovations and green supply chain management: Crafting a novel framework for boosting ecological quality in China

    Liu, XiaoxiZhan, YunqiuSi, DingwenWang, Zhen...
    1.1-1.11页
    查看更多>>摘要：Green supply chain management and renewable technological innovations are integral to sustainable development goal (SDG) 9. Additionally, it serves as the basis for generating eco-friendly energy, indirectly contributing to the achievement of SDG 9. The shift from fossil fuels to green energy sources is crucial for sustainable development and promoting an eco-friendly setting. Therefore, this study examines the major driving forces of ecological quality (proxied by load capacity factor) between 1990Q1 and 2022Q4. Other factors, including natural gas consumption and energy prices, are also studied. The recently proposed quantile-based KRLS and Granger causality are utilized to solve the non-linear and non-normal distribution of the series. The findings of QQKRLS reveal that renewable technological innovations increase load capacity factor (LCF) across all quantiles, thus improving ecological quality. On the other hand, across all quantiles, natural gas consumption, energy prices, economic growth, urbanization, and green supply chain management lessen LCF, thus decreasing ecological quality. The QQGC results show that all the regressors (renewable technological innovations, natural gas consumption, energy prices, economic growth, and green supply chain management) can significantly predict LCF across all quantiles. The study formulates policies in line with these findings.
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    Downcycling in circular production through sustainable insurance under cap-and-trade regulation and carbon tariffs

    Chen, ShiWang, MengjieHuang, Fu-WeiChang, Ching-Hui...
    1.1-1.11页
    查看更多>>摘要：This paper presents a contingent claim option model to explore downcycling in circular production under capand-trade regulations and carbon tariffs, within the context of sustainable insurance. By applying a capped call option, the model shows how a life insurer finances a manufacturer producing steel, slag, and pollutants, while explicitly assessing the manufacturer's risk under related climate policy regulations. The manufacturer under cap-and-trade regulations employs carbon capture, utilization, and storage (CCUS) to support downcycling in slag production while exporting steel and slag, subject to carbon and product tariffs. The findings reveal that increased downcycling enhances the manufacturer's equity but reduces economies of scope. On the other hand, higher carbon emissions lower both equity and economies of scope. A stricter cap in the cap-and-trade system improves equity and strengthens policyholder protection, while increased carbon and product tariffs negatively affect both. Thus, downcycling with CCUS and lower emissions is favorable for the manufacturer's equity. A stricter cap supports efforts to achieve SDG 7 (Affordable and Clean Energy), but carbon and product tariffs do not. For the insurer, a more stringent cap encourages sustainable insurance aligned with SDG 3 (Good Health and Well-Being), though higher tariffs diminish policyholder protection.
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    New challenges for green finance and sustainable industrialization in developing countries: A panel data analysis

    Jawadi, FredjPondie, Thierry M.Cheffou, Abdoulkarim Idi
    1.1-1.9页
    查看更多>>摘要：This paper uses the fixed-effect model, the S-GMM, and the Kinky least square method to investigate the effect of green finance on sustainable industrialization for a panel of 56 developing countries over the period 2000-2021. Accordingly, we propose a per region panel data analysis of the relationship between green finance and sustainable industrialization for three different regions: Africa, Asia, and South America. Our findings show that green finance contributes positively and significantly to improving sustainable industrialization in most of the developing countries under consideration, but that the effect is more pronounced for Asia and South America. This suggests that for a robust environmental result, these developing countries should make greater use of environmentally friendly sources of finance, which can help to reorient their industries toward greater sustainability as well as fight the main economic challenges: unemployment, poverty, inequality, and social injustice.
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    Liberalization of upstream productive services and green innovation in downstream manufacturing firms: Evidence from China

    Liang, WenjingYu, WeihuaYao, Xin
    1.1-1.10页
    查看更多>>摘要：The interplay between openness and green development is a critical issue for developing countries. This article leverages Chinese foreign investment liberalization policies in productive service industries to construct a relatively exogenous index of productive service sector liberalization and uses micro-level data from Chinese manufacturing firms to empirically examine the causal economic implications of productive service liberalization on green innovation in downstream manufacturing firms. The findings indicate that liberalization of the productive service sector boosts green innovation in manufacturing firms. Moreover, the liberalization of the productive service sector enhances green innovation in downstream manufacturing enterprises mainly by enhancing management efficiency, reducing costs, and alleviating financing constraints. The extent of this impact varies according to the geographic location, industry attributes, and other firm-specific characteristics. Furthermore, the presence of stringent environmental regulations and strong intellectual property protection enhances the advantages of liberalizing the productive services on green innovation.
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    Energy policy diversity and green bond issuance around the world (vol 128,107116,2023)

    Mertzanis, Charilaos
    1.1-1.2页
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    Does artificial intelligence improve energy efficiency? Evidence from provincial data in China

    Li X.Li S.Cao J.Spulbar A.C....
    1.1-1.11页
    查看更多>>摘要：© 2024As global energy demand rises and environmental awareness increases, improving energy efficiency (EE) has become crucial to achieving sustainable development. This paper employs a two-way fixed effects panel model using data from 30 provinces in China, from 2000 to 2021, to investigate the impact of artificial intelligence (AI) on EE. The research results reveal that advancements in AI have greatly facilitated the improvement of EE. Furthermore, green technology innovation capability plays a positive moderating role between AI and EE. A heterogeneity analysis indicates that the impact of AI on EE is more significant in economically-developed regions. In energy-deficient regions, AI can significantly improve EE, whereas conversely, in energy-abundant regions, AI's impact on EE is negative. Further analysis using a spatial Durbin model (SDM) confirms the presence of spatial effects in the impact of AI on EE. This paper aims to expand the scholarly understanding of the relationship between AI and EE and provides empirical evidence for decision-makers during this critical period of energy transition. By delving into the potential of AI to enhance EE, the paper seeks to illuminate specific strategies and approaches for policymakers and industry participants.
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    The asymmetric response of higher-order moments of precious metals to energy shocks and financial stresses: Evidence from time-frequency connectedness approach

    Gao, WangJin, XiaomanZhang, HongweiHe, Miao...
    1.1-1.25页
    查看更多>>摘要：This paper analyzes how the higher-order moments of precious metals respond asymmetrically to energy shocks (including demand, supply, and risk shock) as well as financial stresses (such as credit, equity valuation, safe assets, funding, and volatility) using a Time-Varying Parameter Vector Autoregression (TVP-VAR) time-frequency approach. The findings reveal that financial stresses and energy shocks significantly impact the higher-order moments of precious metals, with more pronounced long-term effects. The skewness of precious metals responds positively to these shocks, indicating effective hedging properties, while their volatility remains relatively stable. In contrast, the kurtosis of precious metals shows a substantial short-term reaction but becomes more negative in the long term. Additionally, network spillover analysis indicates that financial stresses-particularly equity valuation and volatility-are the primary sources of net spillovers, while energy shocks, especially risk shocks, function as intermediaries. Gold and platinum exhibit higher-order moments that initially bear the pressure of these shocks, whereas palladium and silver's higher-order moments act as ultimate absorbers of the disturbances. The dynamic spillover effects demonstrate significant time-varying characteristics in the responses of precious metals' higher-order moments. There are indications of increased asymmetry during crisis periods, such as the COVID-19 pandemic and the Russia-Ukraine conflict. Our research highlights the importance of considering the higher-order moments of precious metals for optimizing risk hedging and asset allocation strategies in portfolio management.
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