Energy Efficiency最新文献

Despite the pressing need to accelerate renewable energy consumption (RE/REC) to achieve universal energy access and climate goals, the critical role of women as primary energy users remains underexplored in contemporary literature and energy policy. This study investigates the relationship between women’s empowerment (WE) and renewable energy consumption in Ghana, a focal point in Sub-Saharan Africa. Using the Autoregressive Distributed Lag (ARDL) cointegration technique based on annual time-series data from 1980 to 2020, we find that women's socio-economic and political empowerment positively influence REC in both the short and long run but the effect is significant with the former. Additionally, GDP per capita, foreign direct investment, and human capital enhance REC, whereas urbanization exerts a negative effect. We also use Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) to support our findings. Further analysis using Granger Causality test shows a unidirectional link from WE to REC. Finally, we use a novel Kernel-based Regularized Least Squares (KRLS) approach to show that REC increases at higher levels of WE. The study recommends implementing gender-sensitive financing, supporting women-led renewable energy enterprises, and enhancing female participation in energy governance to leverage WE for sustainable energy transitions in Ghana.

Energy Efficiency (EE) and Energy demand reduction (EDR) policies are crucial for addressing both energy security and climate change. However, despite their strategic importance, they often face significant barriers, including institutional resistance, limited funding, and short-term political priorities that tend to overlook long-term efficiency gains. This study utilizes fuzzy cognitive maps (FCMs) to model the factors influencing EE/EDR policy effectiveness across European countries. To capture the importance and interrelation of factors predefined from the literature, expert knowledge was leveraged through interviews with European energy agency representatives. Qualitative expert assessments were then transformed into numerical values, generating weighted causal matrices. Centrality measures subsequently identified key factors within an aggregated European model. Results revealed six pivotal factors: continuous financial support, favourable regulatory frameworks, consumer engagement, ease of implementation, long-term policy mandates, and support from industry and stakeholders. Scenario analysis explored the impact of three policy interventions: increased market actor support, weakened monitoring frameworks and increased energy literacy for end-consumers. The results of the interventions indicated that isolated interventions had limited impact on overall EE/EDR system outcomes, underscoring its complexity. However, consumer literacy initiatives mitigated negative behavioural effects, such as rebound effects and misaligned targeting, while weakened monitoring frameworks diminished policy coherence and increased redundancies. These findings reinforce the need for long-term policy stability, regulatory clarity, and robust end-user engagement. A systems-based approach, accounting for interdependencies and system dynamics, is crucial for effective EE/EDR policy design, as isolated interventions are insufficient.

Since Russia invaded Ukraine in 2022, the security and sustainability of energy supply have been seriously highlighted. Approximately 90% of an urban context is residential buildings that demand a large amount of heating energy; therefore, predicting energy consumption is essential for successful energy supply and decision-making. This study aims to evaluate machine learning models for predicting the heating energy consumption for residential buildings in a cold climate, focusing on natural gas consumption for space heating and domestic hot water. Linking the building’s physical characteristics to socio-cultural and occupant behavioral characteristics, a novel dataset was developed in which 44 independent relevant variables were analyzed. The results indicate that XGBoost achieved the best performance with an MAE of 2.00, MSE of 2.61, RMSE of 1.61, and R2 of 0.90, followed by RF with an MAE of 1.32, MSE of 2.59, RMSE of 1.61, and R2 of 0.89, while ANN and LR showed lower performance. The feature importance analysis method identified the key variables significantly affecting heating energy consumption; therefore, among the building physics variables, space heating system (HVAC), total unit area, conditioned unit area, building age, and type of thermal insulation were the most effective predictors. Accordingly, among the socio-cultural and occupant behaviors, blocking the cooler channel in the cold seasons was the most effective variable. These findings can guide energy policymakers in designing sustainable heating strategies and assist architects and residents in optimizing energy use for cost savings and efficiency in cold climates.

The ‘Federal Funding Scheme for Energy and Resource Efficiency in the Economy’ (EEE) is a key programme supporting German companies in their transition to climate neutrality. The multi-measure programme funds various technologies through different funding modules, including technology-open funding. This paper presents results from five evaluation rounds (2019–2023) for the first time, extending the empirical basis on funding scheme evaluations for industrial energy efficiency. The methodological framework consists of quantitative and qualitative key performance indicators (KPIs) categorized by core evaluation areas. Particular attention is paid to the results on greenhouse gas savings and funding efficiency. As a second focus, recommendations from the evaluation for future energy and resource efficiency programmes are outlined. The KPI analysis reveals no substantial need for structural revisions: With 2.9 billion euros in funding, the EEE supported 9.7 billion euros in investments from 2019 to 2023. It achieved calculated annual gross GHG savings of nearly 7 million tonnes of CO₂-eq. The evaluation also offers key insights: Establishing a streamlined target system with realistic objectives is important to avoid trade-offs between multiple aims. A stable funding environment, short processing times and clear guidelines support accessibility. Considering evaluation requirements during programme design can enhance data quality for ex-post analyses. Reaching underrepresented groups can be enhanced by engaging multipliers, using new communication channels, and offering targeted support for SMEs. Finally, the evaluation shows that while a technology-open funding approach supports significant savings, technology-focused funding promotes broader engagement and future funding opportunities, underscoring the validity of both approaches in the funding landscape.

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.