This study examines household-level determinants of Liquefied Petroleum Gas (LPG) consumption in India. Using a granular Enterprise Resource Planning (ERP) based dataset, we introduce novel micro-level variables, Subsidy Ratio, Average Log Price Increase, and Adoption Proportion, to examine LPG consumption dynamics. Pooled and fixed-effects regressions reveal substantial heterogeneity along two dimensions: urban versus rural, and enrollment in India’s targeted LPG subsidy program for economically vulnerable households (Pradhan Mantri Ujjwala Yojana, PMUY). Rural and PMUY households are more responsive to subsidies, while urban non-PMUY households exhibit greater price sensitivity, consistent with evidence on the salience of experienced price changes for frequent purchasers. Additionally, this study introduces the A2C2P framework—Accessibility, Affordability, Consistency, Constancy, and Observed Price Change—to provide a structured lens for examining sustained LPG usage behavior by capturing regularity, habit formation, and behavioral responses to experienced price changes. The findings suggest that subsidy design and affordability support may need to be better differentiated across household segments, particularly for PMUY and rural households. These insights provide an analytical foundation for designing more equitable interventions to support clean cooking transitions.
A strong Community of Practice (CoP) can be powerful in supporting people to share, generate, and disseminate knowledge. This study evaluates the use of the Communities of Practice (CoP) approach for effective knowledge consolidation in the field of citizen science. Our paper offers an analysis of four CoPs that were set up as part of the European-based 3-year WeObserve project, with distinct themes of (1) co-design citizen engagement; (2) impact and value for governance; (3) interoperability and standards; and (4) the United Nations Sustainable Development Goals. Participation across the four CoPs fluctuated during their three-year life-time. Three key outcomes emerged from the CoPs. First, a joint identity and understanding were created within and across CoPs through the creation of an inception report by each CoP and through the creation of Citizen observatory (CO) vocabulary, which also served to differentiate such observatories from citizen science (CS) initiatives. Next, scientific papers and technical reports were cooperatively produced by CoP members that represent a synthesis of CoP members’ knowledge. Essential ingredients to the success of these CoPs also included extensive stakeholder engagement and the CoPs being steered by the underpinning values of the CS community. The impacts of the WeObserve CoPs range from the uptake of jointly produced publications, novel cooperative CS projects, new CoPs, joint grant proposals, and the integration of citizen science data into SDG monitoring. This evaluation highlights the diverse and transformative potential of CoPs for citizen science practice.
The paper develops a taxonomy of industry tradability to define the geographical level at which competition takes place. First, it creates a novel dataset that combines production and international trade data for both goods and services industries, defined at a detailed (3-digit) level of industry aggregation for 15 European countries. Then, based on the relative values of domestic sales and international trade flows, it identifies whether each industry is tradable and therefore competes internationally-either globally or within a cross-country economic bloc-or domestically. Each industry is therefore assigned its relevant geographic market boundaries. The proposed classification of industry tradability can be applied in numerous contexts, ranging from the evaluation of trade policies to the assessment of competition.
This study examines public attitudes toward fusion energy in Europe through a comprehensive cross-national survey involving a sample of 19,144 respondents. Following a multidimensional analytical approach, we investigate the distribution of public attitudes, analyse predictive factors influencing support, and assess the impact of information provision on perception. Results reveal predominantly favourable attitudes toward fusion energy, with 57% of participants reporting positive perceptions, 58% expressing acceptance, and 53% supporting expansion following exposure to informational materials. Multiple regression analyses identify several significant predictors of support: pre-existing attitudes toward conventional nuclear power demonstrate substantial predictive influence, while affective responses and perceived environmental benefits emerge as critical determinants. Experimental manipulation of information provision produces statistically significant effects on attitudinal distributions, with recipients of detailed information on the technology's consequences demonstrating higher support (55.4% vs. 51.6%) compared to those receiving general information. More substantially, support increases significantly from baseline to post-information assessment (mean support on a 1-5 scale rose from M = 3.06 to M = 3.52). These findings illuminate the complex interplay of factors shaping public evaluations of fusion energy and carry implications for communication strategies and public engagement initiatives as the research on this technology progresses toward the commercialization phase.
The prediction of power plant performances has traditionally relied on complex thermodynamic models, which incorporate numerous assumptions and operating parameters. As a result, evaluating their energy performance at design or part-load conditions demands significant computational resources to solve complex systems of non-linear equations. Machine learning approaches offer a potential solution to reduce this computational burden. This study predicts the part-load behaviour of combined-cycle gas turbines using a method that integrates thermodynamic and artificial neural network models. The approach involves the random generation of input variables, representing power plant operating conditions. Output variables, including energy and economic performance indicators, are then evaluated through rigorous thermodynamic simulation. Artificial neural networks are trained and validated using these datasets, and their ability to replicate thermodynamic models is assessed using statistical performance metrics. The method is applied to a three-pressure and reheat combined-cycle gas turbine, evaluating part-load performance under varying ambient conditions and considering a part-load strategy based on inlet guide vane and turbine inlet temperature variations. The analysis also evaluates the economic revenues from integrating combined-cycle gas turbines into the Italian day-ahead electricity market, considering ambient condition profiles typical of winter and summer days for a hypothetical power station site. The results demonstrate the model’s capability to define optimal part-load strategies and identify bidding strategies that maximize profits in interconnected electricity and gas markets.
