CS17: Energy Efficiency

India's rapid industrial expansion has increased energy demand, with industry accounting for ~40% of total energy consumption. Within this, manufacturing contributing ~50–60% of industrial energy use and significantly to energy-related CO₂ emissions. Enhancing industrial energy efficiency (EE) is therefore critical for competitiveness, energy security and climate mitigation. In response, India introduced the Perform, Achieve and Trade (PAT) scheme in 2012 as a market-based mechanism targeting energy-intensive industries. This study examines the impact of PAT on the technical, energy, scale, and cost efficiency (TE, EE, SE and CE) of Indian manufacturing sector. Employs Data Envelopment Analysis, along with second stage fixed-effects panel fractional logit and Tobit regressions to captures proportional, non-radial, allocative, and scale-related efficiency dynamics and their determinants. Results show substantial improvements in radial TE and EE after PAT implementation However, SBM estimates reveal persistent non-radial and cost inefficiencies in several lagging, resource-intensive states, despite overall improvements in average SBM-TE. Similarly, average SBM EE remains below slack-adjusted radial EE, indicating continuing input slacks. In many states, divergence between CE and cost-minimising EE suggests behavioural over-adjustment toward energy-intensity reduction under regulatory pressure. In contrast, mineral-intensive states such as Jharkhand, Rajasthan, and Chhattisgarh exhibit structurally embedded inefficiencies linked to capital rigidity and limited technological dynamism. Regression results show that firm size positively influences EE while labour productivity shifts from a negative to a positive association after PAT, suggesting policy-induced structural transformation. Overall, while PAT enhances proportional efficiency, a uniform compliance-based approach cannot fully address heterogeneous structural constraints. Future policy cycles should adopt differentiated, state-specific targets aligned with technological potential, industrial structure and institutional capacity, alongside cost-minimization incentives, rational energy pricing, and technological upgrading to ensure sustained and balanced industrial energy transitions.

Accelerating residential energy-efficient (EE) renovations is essential to achieving climate and energy policy targets, yet adoption remains persistently low. This study provides new empirical evidence on the detrimental role of uncertainty in EE renovations, showing that homeowners value two innovative risk-transfer mechanisms: (i) an insurance covering shortfalls in realised savings and (ii) a lowest-price guarantee compensating price differences for equivalent renovations. Using discrete choice experiments among 1'400 Swiss homeowners, we elicit preferences concerning investment costs, expected energy cost-savings, and these two risk-mitigation mechanisms, and translate them into willingness to pay (WTP) distributions. We consider four experiments corresponding to policy-relevant renovation scenarios differing in scope, with each respondent assigned to an experiment corresponding to their priorities. Homeowners' preferences are analysed using random parameters logit (RPL) models, allowing Normally distributed coefficients to be correlated. Our results confirm the central role of upfront cost and expected energy cost-savings in the likelihood of adopting EE renovations. We also show that both mechanisms significantly influence renovation choices in bundled renovation experiments, highlighting the importance of mitigating performance and timing risks arising from uncertainty in such irreversible investments. We identify heterogeneity across building types, with flat owners valuing both risk-mitigation mechanisms in bundled renovation scenarios, whereas house owners mostly value the guarantee. Mean WTP for insurance corresponds to approximately 10% of the expected cost-savings for both building types. For the guarantee, mean WTP represents about 25% of investment costs for house owners and 13% for flat owners. WTP distributions further reveal substantial dispersion among house owners, indicating greater heterogeneity in their valuations. These findings suggest that, beyond traditional financial incentives, policies supporting the development of risk-mitigation mechanisms could play a key role in accelerating residential energy renovations, while also supporting insurers' risk management and sustainability objectives.

Household energy efficiency is central to demand-side decarbonization, yet households differ widely in what motivates them to conserve energy. This study asks which household and dwelling characteristics predict energy-saving awareness, and whether households are motivated primarily by energy-bill savings (cost), climate change mitigation (climate), and/or social norms（others）. Using microdata from Japan's Household CO2 Statistics (2020–2023; over 30,000 observations), we classify households into mutually exclusive motivation groups based on combinations of three stated motives: cost, climate, and social norms.
We first document time trends and regional heterogeneity in the distribution of motivation groups. We then use multivariate statistical models to examine how household characteristics are associated with motivation-group outcomes. Explanatory variables include income, household composition, age, housing type and tenure, floor area, prefecture indicators, and adoption of energy-efficiency and low-carbon technologies (e.g., double glazing, rooftop PV, and home energy management systems), while controlling for year fixed effects.
The analysis is designed to identify clear household profiles for each motivation group. Descriptive patterns for 2020–2023 suggest a decline in "no awareness" households and a rise in cost-motivated awareness, consistent with heightened salience of energy expenditures in recent years.　The key policy implication is that demand-side energy-efficiency outreach and program design should be tailored to each segment: households with no reported awareness may require simple, salient information about energy costs and actionable first steps; cost-driven households may respond more when climate messaging is bundled with immediate co-benefits (bill savings, comfort, and resilience); and households motivated by climate concerns and/or social norms may benefit from practical enablers and normative feedback that facilitate upgrades (e.g., streamlined decision support, easier access to retrofit options, and peer-comparison information). The effectiveness of specific instruments, including financial incentives, warrants further evaluation.

Amid the global energy transition, the large-scale deployment of intermittent renewable energy has accelerated rapidly, reshaping generation portfolios across major economies. At the same time, energy security concerns have led to a partial revival of conventional power sources. The resulting structural mismatch between expanding generation capacity and limited grid flexibility has intensified renewable curtailment and increased the risk of large-scale blackouts and cascading failures. Under growing stochastic volatility and uncertainty, grid operators are shifting from efficiency-oriented evaluation toward reliability-based real-time dispatch decisions. Consequently, performance assessment of grid-connected plants must move beyond long-horizon deterministic efficiency metrics and focus on short-term stability and operational robustness.
To address this challenge, this study proposes a chance-constrained Data Envelopment Analysis (DEA) framework for evaluating short-term generation reliability under uncertainty. Using high-frequency operational data, multidimensional real-time technical indicators are modeled as stochastic input–output variables, and chance-constraints are imposed at a predefined confidence level. Exogenously specified, technology-differentiated reliability satisfaction thresholds are introduced to determine whether plants meet real-time grid access requirements. A rolling time-window mechanism dynamically updates the production frontier and yields stochastic dominance probabilities. 
The empirical analysis is based on operational data from a provincial power system in China, covering coal-fired, hydropower, wind, photovoltaic, and energy storage units. Results reveal substantial discrepancies between deterministic efficiency rankings and stochastic reliability performance. Intermittent renewable units with high average output but significant volatility exhibit lower reliability probabilities under uniform standards, while differentiated satisfaction thresholds better reflect their system roles. Rolling evaluation further shows pronounced temporal variability during peak demand and extreme weather events. These findings highlight the importance of probabilistic reliability screening in real-time dispatch and provide quantitative support for balancing renewable integration, curtailment mitigation, and system security in high-uncertainty environments.

Costs and CO₂ emissions depend directly on the time spent in drilling operations during well trajectory construction. Rental costs and the energy generated on-site by diesel-electric systems depend on the rig time spent at the well location. The longer the operations take, the greater the diesel consumption, and the higher the costs and GHG emissions. However, drilling time varies significantly from one well to another due to differences in rock properties, trajectory design, drilling technology, rig equipment and operational conditions. Due to this complexity, traditional statistical methods are insufficient for adequately analyzing operational results. In this study, regression models that capture these complex relationships are presented, providing a more accurate representation of drilling results in an oilfield or sedimentary basin. Actual results offer new and useful insights for technical, operational, economic and contractual decision-making. Immediate applications include supporting the economic assessment of new technologies, drilling wells for geothermal exploration or BECCS (Bioenergy Capture and Carbon Storage) and managing Turn-Key contracts, enabling economic evaluations based on real field conditions and the technological and financial resources used. Additionally, by reducing the total operational time and greenhouse gas emissions, the method opens an opportunity for operators to claim carbon credits associated with testing and adopting more efficient technologies. The conclusions of this work provide relevant and original contributions at the intersection of drilling engineering and environmental sustainability.