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The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement.

Firms and governments often use moral suasion and economic incentives to influence intrinsic and extrinsic motivations for economic activities. To investigate persistence of such interventions, we randomly assign households to moral suasion and dynamic pricing that stimulate energy conservation during peak-demand hours. We find significant habituation and dishabituation for moral suasion—the treatment effect diminishes after repeated interventions but can be restored to the original level by a sufficient time interval between interventions. Economic incentives induce larger treatment effects, little habituation, and significant habit formation. Our results suggest moral suasion and economic incentives produce substantially different short-run and long-run policy impacts.

In this study, the relationship between electricity and growth of the economy is investigated by applying the newly-developed bootstrap autoregressive-distributed lag test with a Fourier function to examine both the causality and cointegration for China, India, and the United States (US). While it is not possible to detect a long-term cointegration relation among the economy's electricity and growth, the study findings demonstrate the contingency of the causality. The ensemble method in machine learning performs better than conventional methods as electricity is an independent indicator for forecast economics. Concerning the US, previous electricity consumption has a positive impact on the current nature of economic growth. In contrast, the consumption of electricity is negatively affected by the development of the economy. However, for China and India, positive and negative feedback can be observed, respectively. Due to the increased awareness of the environment's adverse effects, China should promote technologies that conserve energy and boost energy efficiency to achieve sustainable development in both environmental and economic terms. In India's context, broadening access to electricity has significance for residents in rural areas and enhances economic growth. It is recommended that policy-makers promote innovative technologies in the US, as the abundant natural and human resources can make valuable contributions to the society and development of the economy.

Amidst a dynamic energy market landscape, understanding evolving influencing factors is pivotal. Accurate forecasting techniques are indispensable for effective energy resource management. This study focuses on illuminating insights into economic uncertainty and commodity price forecasting. A meticulously curated dataset spanning January 2000 to December 2022 forms the foundation, incorporating diverse economic and financial uncertainty metrics. Through an innovative research framework, we discern influential factors and forecast their trajectories. Three deep learning models—Short-Term Memory, Gated Recurrent Units, and Multilayer Perception Network—are deployed. The Multilayer Perception model emerges as the standout, showcasing exceptional predictive capability rooted in its adeptness at decoding intricate market patterns. This finding holds significance for policymakers, industry experts, and energy economists. The Multilayer Perception model’s supremacy offers a robust tool for decision-making in crafting economic policies and navigating volatile markets.

Belt and Road Initiative (BRI) countries are major energy producers and consumers in the world, and they have enormous potential for energy cooperation, energy saving, and CO2 emissions reduction due to their various resource endowments. However, little quantitative research has been conducted under the BRI in the same framework. Therefore, by developing a data envelopment analysis optimisation model combined with the window analysis method, this paper investigates the energy performance of BRI countries for the period from 1995 to 2015, and evaluate the potential of energy saving and CO2 emissions reduction for each BRI country. The results show that, first, the average energy performance of 56 BRI countries is about 0.69, with evident difference across regions and countries. Specifically, in Sub-Saharan Africa and Europe and Central Asia, energy performance is relatively lower, and their averages are 0.59 and 0.60, respectively; in particular, Ukraine has the lowest energy performance among the 56 BRI countries (0.24); while the energy performance in Middle East and North Africa and South Asia appears relatively higher (0.80 and 0.89, respectively). Second, these 56 BRI countries have great energy saving potential, about 9.95 billion metric tonnes of oil equivalent from 1995 to 2015. Among them, Europe and Central Asia, East Asia and Pacific, and Middle East and North Africa make relatively larger contribution. Finally, these 56 BRI countries may produce potential CO2 emissions reduction of 50.87 billion metric tonnes during the study period, and Europe and Central Asia and East Asia and Pacific contribute the most (45.18% and 25.53%, respectively).

\"Nudges\" are being widely promoted to encourage energy conservation. We show that the popular electricity conservation \"nudge\" of providing feedback to households on own and peers' home electricity usage in a home electricity report is two to four times more effective with political liberals than with conservatives. Political conservatives are more likely than liberals to opt out of receiving the home electricity report and to report disliking the report. Our results suggest that energy conservation nudges need to be targeted to be most effective.

This article investigates the rebound effect in residential heating, using a sample of 563,000 households in the Netherlands. Using instrumental variable and fixed-effects approaches, we address potential endogeneity concerns. The results show a rebound effect of 26.7% among homeowners, and 41.3% among tenants. We corroborate the findings through a quasiexperimental analysis, using a large retrofit subsidy program. We also document significant heterogeneity in the rebound effect, determined by household wealth and income, and the actual energy use intensity. The findings in this article confirm the important role of household behavior in determining the outcomes of energy efficiency improvement programs.

An argument sometimes used to support renewable energy is that it may contribute to job creation. On the other hand, these technologies often face local opposition. In the case of Denmark, the country with the longest wind power experience, we examine whether the installation of new turbines had local economic benefits. Using the Danish master data register of wind turbines and detailed data on the municipal budget, personal income and sectoral employment from Statistics Denmark, we build a panel covering 250 municipalities. We use a quasi-experimental set-up and exploit time and regional variations at the municipal level. We find that the deployment of wind power contributed to the increase in personal income for entrepreneurs and reduced dependence on social benefits. As municipalities received payments from wind investors ahead of the construction, the new wind revenues were also followed by increases in local public spending. We find only very minor effects on employment in some sectors, and the aggregate local employment does not change significantly. Heterogeneity analyses indicate that the increases in local entrepreneurial income are largely driven by small installations, whilst increases in municipal budget and reductions in the dependence on social benefits are induced by larger installations.

Urban governance is an important cornerstone in the modernization of a national governance system. The establishment of smart cities driven by digitalization will be a vital way to promote economic green and sustainable growth. By using the data of 274 prefecture-level cities in China from 2004 to 2017, we study the impact of smart city policy on economic green growth and the underlying mechanism of the impact. It is shown that the establishment of smart cities has significantly promoted the green growth of China’s economy. This conclusion is further confirmed by using exogenous geographic data as instrumental variables and robustness tests, such as the quasi-experimental method of Difference in Difference with Propensity Score Matching (PAM-DID). The mechanism test shows that promoting economic growth, reducing per unit GDP energy consumption, and lowering waste emissions represent three ways for smart cities to promote green economic growth. The heterogeneity test shows that smart city policy has an obvious promotional effect on the economic green growth of both large cities and non-resource-based cities. This paper is expected to provide a reference for the urban development and economic transformation of emerging economies.

Biomass energy is critical to future low-carbon economic development facing the challenge to mitigate the high carbon emission from conventional energy exploitation. Biomass energy developed from energy plants will play a more important role in future energy supply in China. As cultivated land resources are limited and critical to food security, the development of energy plants in China should rely on the exploitation of marginal land. In this study, based on three scenario-based (RCP2.6, RCP4.5 and RCP8.5) land cover datasets, the Net Primary Productivity (NPP) dataset, the dataset of marginal land suitable resources for cultivating bioenergy crops, and protected area dataset, firstly, we spatially identify and quantify the available areas of three types of marginal land, including abandoned agricultural land, low-productivity land and the ‘rest land’; then, the geographical potentials of biomass energy are calculated through multiplying the available area for energy plants by the corresponding productivity out to 2100 in China. The results show that significant potentials for biomass production are found in the south of China, such as Yunnan, Sichuan, Guizhou and Guangxi provinces. The total geographical potential biomass energy of the marginal land ranges from 17.813 to \\[19.373\\, EJ\\, year^-1\\] under the three scenarios, reaching the highest under RCP8.5 scenario, and the geographical potential biomass energy of the ‘rest land’ is the largest contributor, accounting for more than 90% of the total potential biomass production.