Energy Efficiency最新文献

Wind power is the fastest-growing renewable energy source and presents huge development prospects. Most existing literature employs linear methods to investigate wind power, often overlooking the nonlinear relationships among economic variables. Unlike previous studies, this article employs a novel semiparametric model to investigate the nonlinear impact and mechanism of green finance on wind power. Empirical results show that green finance exerts a positive U-shaped effect on wind power, suggesting that the driving role of green finance in wind power is gradually becoming prominent over time. Heterogeneity analysis shows that green finance generates an inverted U-shaped impact on wind power in the eastern region, while its impact in the central and western regions presents an N-shaped and a positive U-shaped impact, respectively. From the perspective of production scale, green finance produces a positive U-shaped impact on wind power with medium to low production, and an M-shaped impact on wind power with high output. Mechanism analysis shows that green technology innovation yields a positive U-shaped impact on wind power, while foreign direct investment generates an inverted U-shaped impact on wind power. In addition, the empirical results also show that economic growth, environmental regulations, urbanization, and fossil fuel prices have a push impact on wind power, while power prices and fiscal decentralization have a constraining effect. The policy recommendations derived from the research findings can provide policy references for the formulation of new financial and industrial policies.

This study examines China’s carbon abatement costs and the role of digital technology, using provincial panel data from 2000 to 2021. By distinguishing between clean and non-clean energy inputs, we find that the estimated carbon abatement cost significantly exceeds prevailing market trading prices and follows a U-shaped temporal pattern—declining initially and then rising steadily. Our analysis shows that digital technology positively influences carbon abatement costs, primarily through improvements in energy efficiency. This effect varies regionally, with the strongest impacts observed in Central China—an unexpected finding given the conventional emphasis on coastal regions. These insights have important policy implications: (1) carbon pricing mechanisms should be reformed to more accurately reflect the true social cost of emissions; (2) the adoption of clean energy must be accelerated to reduce disparities in abatement costs; and (3) targeted digital investments, particularly in inland provinces, can enhance the effectiveness of emissions reduction strategies. By integrating energy-source differentiation with the dynamics of digital transformation, this study offers a more refined framework for evaluating carbon abatement costs and highlights the need for regionally tailored policies to achieve China’s 2060 carbon neutrality goal.

To address the limitations of existing Non-Intrusive Load Monitoring (NILM) methods in capturing the multi-scale variability and spatiotemporal dependencies of appliance power consumption, this paper proposes a novel progressive temporal convolutional architecture, ChronoFuse-TCN. The proposed model adopts a multi-stage feature extraction strategy to progressively enhances its ability to represent and interpret appliance-level load patterns. By combining dynamic multi-scale modeling, long-range temporal context encoding, and spatiotemporal attention mechanisms, the proposed approach enables more effective separation of overlapping and dynamic power signals. Furthermore, cross-stage feature integration is employed to enrich the hierarchical representation of load features. Experimental results on the UK-DALE dataset show that ChronoFuse-TCN achieves significantly lower disaggregation error compared to state-of-the-art baselines, demonstrating its effectiveness and generalization capability in complex NILM scenarios.

This study examines the impact of trade liberalization on the transformation of energy consumption among Chinese industrial enterprises, with implications for sustainable economic growth, environmental protection, and energy efficiency. Employing a Difference-in-Differences (DID) approach, we analyze panel data from China’s Industrial Enterprise and Pollution Emission Databases to assess the effects of trade liberalization. To explore underlying mechanisms, we incorporate mediation analysis to disentangle scale and technique effects. Our findings indicate that trade liberalization significantly promotes energy consumption transition by enhancing energy efficiency, primarily through technological upgrading and economies of scale. The regional heterogeneity analysis finds that enterprises in the eastern region, coastal areas, and transportation hubs benefit more from trade liberalization. Industry-level analysis reveals that technology-intensive enterprises and low-energy-consumption industry respond more positively, reflecting higher absorptive capacities for foreign technologies and stronger incentives for innovation. Firm ownership also plays a key role. Individual and corporate enterprises exhibit more substantial responses than state-owned and foreign enterprises, highlighting the importance of managerial flexibility and market-driven incentives in adopting energy-efficient practices. Large enterprises are better able than small and medium-sized enterprises to improve energy efficiency in response to trade liberalization. Overall, the study offers robust evidence that trade liberalization can serve as a catalyst for green industrial upgrading in emerging economies. The results provide actionable insights for policymakers aiming to align trade and environmental objectives in China’s next phase of sustainable development.

Energy audits (EAs) and Energy Management Systems (EnMS) are crucial instruments for companies to identify and implement energy efficiency measures (EEMs), thereby contributing to the EU’s climate and energy objectives. The updated Energy Efficiency Directive (EU/2023/1791) strengthens the role of these tools and introduces new provisions under Art. 11. Among these, the directive establishes specific consumption thresholds, requiring the adoption of EnMS for businesses with high energy usage and mandating EAs for other energy-intensive entities. Companies subject to EAs must develop annual implementation plans to systematically adopt the EEMs identified. This paper investigates how EEMs have been implemented under Art. 8 of the Energy Efficiency Directive (2012/27/EU) in ten European countries and explores how existing practices can inform the upcoming obligations introduced by Art. 11 of the revised Directive (EU/2023/1791). The primary aim is to assess the effectiveness of national data collection systems, evaluation methods, and policy tools in supporting the adoption of EEMs by companies. To this end, in 2024, national experts from ten EU member states responded to a targeted questionnaire focused on methodologies and practices related to the implementation of EEMs under the obligations of Art. 8. The study identifies current data availability and transparency practices, evaluates existing indicators and the role of EA guidelines, implementation plans, and facilitating factors. Good practices in the 10 European countries under analysis are also identified and described. Findings show significant variation in how countries collect and publish data, with some demonstrating advanced practices such as centralised databases or audit follow-up requirements. The paper identifies a set of good practices and emphasises the value of stronger coordination and more standardised approaches, particularly in view of the new obligations under Art. 11. By providing insights into current framework, the paper aims to support policymakers and energy agencies in enhancing the effectiveness of EAs and EnMS in driving the implementation of EEMs, thereby contributing to improved energy policy outcomes across Europe.

A diverse set of policy instruments targets residential energy use, including building codes, energy performance standards, labels, energy taxes, and subsidies. While bottom-up evaluations suggest these instruments achieve energy savings, top-down evaluations do not always confirm the same results. This discrepancy arises because bottom-up evaluations often rely on assumption-based deemed savings, while top-down analyses may obscure savings due to structural dynamics that cannot be easily isolated. To bridge this gap and better understand the impact of energy efficiency policies within broader energy consumption trends, this study analyses Dutch residential energy use from 2020 to 2023 within the framework of the EU Energy Efficiency Directive (EED). The EED caps total final energy use with an energy efficiency target (Article 4) while imposing an end-use energy savings obligation (Article 8), either by establishing an energy efficiency obligation scheme (Article 9) or by adopting alternative policy measures (Article 10). Our analysis covers two years affected by COVID-19 (2020 and 2021) and two years of elevated energy prices (2022 and 2023). Using chained additive index decomposition analysis, we assess the Article 4 efficiency target top-down by quantifying key drivers: volume, structural, and efficiency effects. We then synthesize the results of 2020 and 2022 with the bottom-up figures reported under the Article 8 energy savings obligation, isolating the loss of energy savings due to the COVID-19 lockdowns and the energy savings from behavioural changes triggered by the energy price shock. Our findings show that bottom-up and top-down evaluations complement each other. Bottom-up analysis helps disentangling efficiency effects in top-down evaluations, while top-down analysis contextualizes bottom-up policy impacts and can potentially be used for consistency checks. Combining these approaches can provide a clearer assessment of the contribution of (combined) energy efficiency policies to climate goals.

Data collection and analysis on the implementation of energy efficiency measures (EEMs) under Article 8 of the Energy Efficiency Directive (EED) vary widely across European countries. This paper focuses on the management and enforcement of Energy Audits (EAs) obligation, and the associated EEMs information, in Portugal and Italy, two countries with distinct approaches. Specifically, the study delves into the Portuguese SGCIE (Sistema de Gestão dos Consumos Intensivos de Energia), and the implementation of the Italian Legislative Decree 102/2014 along with Ministerial Decree 256/2024 (Decreto Energivori). In Portugal, SGCIE plays a pivotal role in monitoring energy-intensive installations, mainly from the industrial sector, fostering the adoption of EEMs through a mandatory framework. This paper investigates how SGCIE collects data from EAs, enforces the implementation of EEMs, and tracks energy savings, contributing to national and EU energy efficiency goals. Similarly, the study delves into the Italian framework, analysing EAs obligations governance and EEMs data managing, particularly for energy-intensive industries. Emphasis is placed on the effectiveness of these mechanisms in gathering and utilizing information on EEMs. This analysis highlights the strengths of each system, underscoring key differences in how Portugal and Italy have transposed Art. 8 EED obligations into national law. The findings show that both countries have developed robust digital systems to collect and analyse EEMs data, offering valuable insights into energy consumption trends and policy impacts. These approaches improve data quality, support company-level energy management, and provide a foundation to meet the more stringent requirements of the revised EED (EU/2023/1791).

Evaluating energy efficiency (EE) in the manufacturing sector at the national level is analytically challenging due to the sector's heterogeneity and the limitations of commonly used indicators. Despite manufacturing’s central role in industrial decarbonization, there is no comprehensive overview of the quantitative methods used to assess its EE. This study addresses this gap by systematically reviewing 110 peer-reviewed studies published between 2005 and 2024, focusing on the evolution, application, and reliability of ratio-based indicators, decomposition techniques (Index and Structural Decomposition Analysis), frontier methods (Data Envelopment and Stochastic Frontier Analysis), and econometric approaches. The review reveals a strong preference for econometric analysis, followed by ratio indicators and index decomposition, while frontier and structural decomposition techniques remain underused. We identify four key challenges that can affect the robustness of EE assessments: definition inconsistencies in conceptualizing EE, data limitations affecting disaggregation and comparability, potential misalignment between methods and data, and interpretation challenges when translating findings into policy insights. Our systematic assessment indicates that most studies fall short of good methodological practices, with ratio-based methods performing strongest overall, suggesting that robust EE assessment requires (a) methods aligned with specific contexts, (b) sufficiently disaggregated data, and (c) awareness of methodological limitations. This review offers a framework for addressing methodological challenges in manufacturing EE analysis, improving the reliability of information available to policymakers for effective EE interventions.

This article emphasizes the crucial role of measuring corporate carbon footprints and the associated economic benefits. Through an in-depth case analysis of a major facility, the study thoroughly examines the transformative impact of eco-design applications in pump manufacturing. By analyzing the implementation of EU 547/2012 (EU547) and EU 547/2012 EPA (EU-EPA) regulations, the study demonstrates significant achievements in carbon emission and energy efficiency improvement. The organization's commitment to environmental responsibility has resulted in significant carbon footprint reduction through the use of eco-design pumps. These sustainability measures contributed to a 4.8% reduction in total carbon emissions originating from pump usage, further highlighting broader environmental benefits. The article provides practical insights and quantitative evidence for organizations striving to balance ecological responsibility and economic sustainability by emphasizing the significance of sustainable design in the industrial sector. Furthermore, the study explores the financial dimensions of sustainability, showcasing how even modest investments can yield substantial returns. Under a hypothetical scenario where all pumps sold are eco-designed, potential savings of 1,092,437 USD are projected.

Standard thermal comfort models often fail to capture individual thermal sensations and offer limited interpretability for practical use. This study presents a building-specific, occupant-centric approach that combines self-reported comfort data with interpretable machine learning. The methodology is demonstrated through a case study involving self-reporting campaigns conducted during summer and winter seasons, accompanied by the development of a random forest regression (RFR) model. We employ three IML techniques namely Partial Dependence Plots (PDPs), SHAP values, and surrogate models to enhance the understanding of this RFR model. These interpretative tools facilitate a deeper understanding of the factors influencing thermal comfort, enabling targeted interventions for energy savings and improved occupant satisfaction. While the methodology offers a replicable framework for occupant-centric building control systems, it acknowledges limitations such as reliance on subjective self-reporting and the exclusion of architectural features. This research emphasizes the importance of integrating interpretable machine learning techniques to balance accuracy and usability, laying the groundwork for energy-efficient and occupant-focused indoor environmental management.