Search Results (253)

Increasing load demand and localized constraints are driving the need for cost-effective alternatives to traditional network reinforcement. However, existing Non-Wires Alternative (NWA) planning approaches often rely on simplified assumptions or computationally intensive full-year optimization, limiting their practical applicability. This study proposes a planning-oriented method integrating 8760-h Direct Load Flow (DLF)-based assessment, worst-case screening, and Mixed-Integer Linear Programming (MILP)-based resource sizing for the coordinated deployment of Energy Storage Systems (ESSs), Demand Response (DR), and Photovoltaic (PV) resources, along with building-scale microgrid candidates. The proposed microgrid candidates are modeled as grid-connected, building-scale configurations in which PV, ESSs, and DR are co-located at a single node, representing integrated resource units within the distribution system. The results show that voltage constraints are the dominant limiting factor and that NWAs primarily function as an investment deferral strategy rather than a full replacement for traditional reinforcement, delaying constraint violations by approximately 2 to 14 years. An ESS provides the most direct contribution to constraint mitigation, while DR and PV offer complementary support. The results also highlight the importance of locational deployment. In particular, a co-located microgrid configuration (MG_111) is selected as the optimal portfolio under moderate load growth conditions (Case B, 2%), demonstrating the practical feasibility of integrated DER deployment at a single node. Economic feasibility is found to be highly sensitive to incentive design, with profitability achieved only under favorable compensation conditions. These results demonstrate that coordinated DER portfolios can effectively extend deferral periods and provide practical insights into cost-effective NWA planning under realistic operating conditions. Full article

This paper examines the effect of a bonus cap on the compensation structure of top earners in the Dutch banking sector. Following concerns that performance-based pay may induce excessive risk-taking, regulators introduced caps on variable compensation. This paper analyzes how such regulation affects the composition of pay. The identification strategy exploits a unique institutional setting in which banks with their statutory seat in the Netherlands are subject to a stricter bonus cap than banks headquartered in other EU countries, while operating in the same market. This paper uses administrative microdata and a difference-in-differences approach to compare compensation outcomes across these groups before and after the introduction of the Dutch bonus cap in 2015. Consistent with the predictions of a principal–agent model of incentive contracting, the hourly variable wage decreases by 23 percent, while the hourly fixed wage component increases by 12 percent. The findings indicate that compensation regulation reshapes the composition of pay. Full article

Atmospheric pollutants and CO₂ share common origins in fossil fuel combustion, raising the question of whether fiscal incentives targeting air quality alone can indirectly reduce carbon emissions. This study examines this question by evaluating China’s air quality ecological compensation policy, a provincial-level horizontal fiscal transfer mechanism under which cities are rewarded or penalized according to changes in ambient air quality indicators, without incorporating any explicit carbon-related assessment criteria. Using panel data from 268 prefecture-level cities over 2007–2023 and a multi-period difference-in-differences design, we find that the policy significantly reduces the composite pollution carbon index (β = −0.213, p < 0.01), with the effect confirmed by an alternative weighted-average specification (β = −0.153, p < 0.01) and robust to propensity score matching, one-period lagged regression, exclusion of provincial-level municipalities, and exclusion of the COVID-19 period. A two-step mechanism analysis, adopted to avoid post-treatment bias from “bad controls,” reveals that the policy promotes industrial structure upgrading (β = 0.253, p < 0.01), enhances green technological innovation capacity (β = 0.047, p < 0.10), and reduces energy consumption intensity (β = −0.012, p < 0.01). Heterogeneity analysis based on quartile subsamples shows that the synergistic benefits concentrate in cities with stronger fiscal capacity (β = −0.349, p < 0.01 versus insignificant for low-support cities), higher economic development, and greater urbanization (β = −1.558, p < 0.01 for highly urbanized cities), while the policy effect is statistically insignificant in the least-advantaged subgroups across these three dimensions. In contrast, the green coverage dimension reveals an opposite pattern: the effect is strongest in cities with lower green coverage (β = −0.378, p < 0.05) and insignificant in high-coverage cities, indicating diminishing marginal returns where environmental baselines are already favorable. These findings highlight the need for differentiated compensation standards, including tiered compensation coefficients and targeted fiscal support for resource-constrained regions, to ensure equitable governance outcomes. Full article

Incentive-based demand response (DR) programs rely on accurate and trustworthy quantification of customer performance to ensure fair compensation and market efficiency. Estimating the customer baseline load is an important part of this process. It shows how much electricity would be used if there were no DR occurrence. Unlike conventional load forecasting, baseline modeling is inherently unobservable, economically sensitive, and vulnerable to strategic manipulation. With the growing penetration of distributed energy resources, electric vehicles, and intelligent control technologies, traditional baseline estimation approaches face increasing limitations. This paper offers a thorough and future-oriented synthesis of baseline load estimation for incentive-based DR strategies. Current approaches are carefully classified into rule-based, statistical, probabilistic, machine learning (ML), and hybrid intelligence techniques, and their appropriateness for various DR services and client categories is rigorously evaluated. Beyond modeling accuracy, this paper emphasizes market-oriented requirements, including incentive compatibility, simplicity, transparency, privacy preservation, and deployment feasibility. Furthermore, emerging digital trust enablers such as blockchain and FL are reviewed, along with baseline-free and baseline-light alternatives for performance evaluation. Finally, open research challenges and future directions toward interpretable, robust, and market-ready baseline intelligence are discussed. Full article

Background/Objectives: Dental Practice-Based Research Networks (DPBRNs) bridge the gap between academic research and private dental practice, addressing questions relevant to everyday medical care. Despite their growing scientific output, little research has explored the experiences of practitioners engaged in these networks. Our study therefore aims to investigate these practitioners’ perspectives in order to identify strategies for improving investigator recruitment, training and data quality in future DPBRN studies. Methods: The qualitative methodology was chosen, and our study adhered to the Standards for Reporting Qualitative Research (SRQR) guidelines. Semi-structured interviews were conducted with dentists who had participated in a DPBRNs study and transcribed before being thematically analysed using Braun and Clarke’s framework. MaxQDA 2022 software was used to facilitate coding of the verbatim quotes. Results: Three major themes emerged: (1) obstacles to participation, including time constraints, difficulties in patient recruitment, and a perceived disconnect between academia and private practice; (2) facilitators of engagement, such as strong leadership, logistical support, and a collaborative research environment; and (3) personal benefits, such as skill development, breaking professional routines, and counteracting stereotypes about private practitioners’ involvement in research. Conclusions: The findings align with existing literature on medical Practice-Based Research Networks (PBRNs), highlighting logistical and motivational barriers while also emphasizing the importance of social and professional benefits. Notably, although financial compensation or credits for continuing professional development are frequently cited as motivators for research participation, these were not significant concerns for our participants. This study sheds light on the experiences of health practitioners in PBRNs, offering recommendations to overcome challenges through strategies such as accessible training, practical incentives and collaboration opportunities. Full article

The rapid growth of distributed energy resources (DERs) is transforming distribution networks and increasing the need for coordinated flexibility management to maintain secure and economically efficient operation. In this work, we examine how uncertainty in load demand and photovoltaic (PV) generation affects incentive-based flexibility coordination within a hierarchical three-level framework. The proposed architecture integrates household energy management systems (HEMSs), an aggregator responsible for incentive allocation, and a distribution system operator (DSO) model based on AC optimal power flow. To account for demand and PV variability, a $\mathrm{\Gamma }$-budget-robust optimization approach is adopted. Also, an incentive–penalty mechanism is introduced to allocate compensation according to each prosumer’s actual flexibility contribution while promoting economic fairness. The entire framework is implemented in PYOMO and tested on the IEEE 33-bus distribution system. A comparative evaluation between deterministic and uncertainty-aware cases is conducted to quantify the cost of robustness and to analyze its influence on flexibility participation, incentive distribution, household net cost, and voltage regulation performance. The results indicate that uncertainty can lead to deviations from initially scheduled flexibility commitments, thereby triggering penalty signals during re-optimization and strengthening contractual compliance. Although the robust formulation results in a moderate increase in operational cost, it substantially improves voltage compliance and overall system reliability. Overall, the findings highlight the importance of explicitly incorporating uncertainty in multi-level flexibility coordination to ensure both technical consistency and practical enforceability in modern distribution networks. Full article

Under tightening land, fiscal, and environmental constraints, hydropower resettlement must shift from a compensation-oriented model toward synergistic development. Existing studies fail to clearly define the boundary between statutory responsibilities and incremental synergistic action, and lack a tripartite quantitative framework capturing the closed-loop interaction among hydropower enterprises, local governments, and resettled households. This study contributes to the literature by explicitly conceptualizing synergistic development as incremental collaboration beyond statutory responsibilities, and constructing a tripartite evolutionary game model with a gradient strategy space from routine compliance to active collaboration. The model incorporates key parameters like additional collaboration costs and synergy premium, and uses replicator dynamics to identify equilibrium regimes and threshold conditions. The results show that reasonable cost–benefit structures enable convergence to a full synergistic equilibrium, while excessive additional collaboration costs lead to low-level compliance lock-in; strategy-dependence coefficients only affect convergence speed, not the long-term evolutionary tendency toward synergy. The findings provide a quantitative basis for designing incentive-compatible resettlement governance mechanisms. Full article

Achieving the “Dual Carbon” goals requires accelerating the near-zero energy transition of office buildings. Existing research focuses on isolated economic or technical dimensions, neglecting the dynamic evolution of tripartite collaboration among the government, developers, and users, and lacking integrated quantification of key drivers like carbon reduction, energy savings, and comfort benefits. To address this, this study develops a tripartite evolutionary game model that incorporates technical parameters, simulated with data from a nearly zero-energy office building (NZEOB). Results show that the transition is stage-dependent, shifting from initial government drive to long-term market sustainment. The economic benefits from energy savings emerge as the core decision factor in current technology adoption, often exerting a stronger influence than carbon reduction benefits, while improvements in comfort can effectively accelerate market acceptance. Technology pathways need to align with developmental stages; low-cost technologies are advisable in the early phase to lower entry barriers, whereas medium to later stages should focus on technologies with high combined benefits and system integration. Policy instruments should be dynamically optimized, with an emphasis on strengthening penalty mechanisms to establish rules in the early stage, shifting toward performance-linked incentives in the mid-to-late stages, and emphasizing compensation mechanisms to build trust. This study offers a basis for multi-party collaboration and stage-adapted strategies. Full article

The northern agro-pastoral ecotone of China faces persistent trade-offs among cultivated land (CL) protection, energy development, water constraints, and ecological restoration, posing challenges for sustainable human–land interactions. Focusing on Yulin City from 1980 to 2020, this study develops an integrated diagnostic framework coupling pattern–process–trend–mechanism modules to analyze the spatiotemporal evolution, transition pathways, and driving forces of CL change. Results show that CL dynamics over four decades were shaped by nonlinear interactions among natural conditions, policies, economic development, and technological progress. Spatially, CL changes exhibited a distinct divergence, with ecological-driven contraction in the southern region and sandy land-based compensation in the north. Temporally, the transformation evolved from a gradual, nature-dominated stage to a policy-intensive phase characterized by abrupt shifts, followed by a refined regulation stage with multi-factor synergies. Policy interventions and economic incentives emerged as dominant drivers of CL spatial heterogeneity, with interacting factors exerting bidirectional effects. Building on these findings, a Zoning–Optimization–Synergy (ZOS) framework is proposed to support adaptive land governance, emphasizing differentiated management and cross-sector coordination. This study offers a transferable diagnostic approach for understanding CL dynamics in fragile ecotones and provides insights for managing the water–energy–food nexus under ecological transition and climate change. Full article

Embracing environmental, social, and governance (ESG) principles is essential for the automotive industry to align with the global low-carbon trend, and carbon emission intensity serves as the core metric for evaluating the sector’s emission reduction effectiveness. We construct a two-way fixed-effects model to assess the influence of ESG performance on carbon emission intensity in the automotive manufacturing sector of Chinese A-share-listed companies from 2009 to 2022 and arrive at the following conclusions: There is a significantly negative relationship between the ESG performance and carbon emission intensity of automotive manufacturing firms, and the finding remains valid according to a series of robustness and endogeneity tests. In addition, small-scale and non-state-owned enterprises appear to focus more on managing carbon emission intensity than their large-scale and state-owned firms do. We also find that ESG performance impacts the carbon emission intensity of automotive manufacturing companies through increased executive compensation incentives, while financing constraints have enhanced the influence of ESG performance on the sector’s carbon emission intensity. Full article

With the rapid expansion of renewable energy and the acceleration of electricity market reforms, coal-fired units are facing increasing difficulty in recovering fixed costs due to marginal cost-based bidding competition and depressed clearing prices caused by low-cost renewable integration, circumstances in which reasonable returns and investment incentives for coal-fired power plants are not guaranteed. To address this issue, this paper proposes a capacity cost compensation mechanism for coal-fired power in the electricity market environment. First, a joint clearing model for the electricity spot market considering both energy and reserve services is established, and annual market operation simulations are conducted to obtain unit output schedules, clearing prices, and annual revenues. Second, based on the long-term simulation results, the marginal clearing probability and fixed cost recovery deficit of each coal-fired unit are calculated, and a capacity compensation pricing method based on marginal clearing probability weighting is proposed to determine the system unit capacity compensation price. Subsequently, the compensated capacity is determined using the availability factor method, comprehensively reflecting each unit’s actual contribution to system capacity adequacy. Finally, case studies conducted on a modified IEEE 30-bus system validate the effectiveness of the proposed mechanism. The results demonstrate that following the implementation of the proposed mechanism, the investment payback periods of all coal-fired units are reduced to within the planned 20-year horizon, thereby ensuring the sustainable operation of coal-fired units and maintaining adequate reliability margins in the power system. Full article

This study examines how firms’ ESG performance is reflected in CEO compensation design. The analysis shows that firms with stronger ESG performance award lower total CEO pay while placing greater emphasis on performance-based and equity-based incentives. Governance quality further strengthens the alignment between CEO pay and firm performance, and these effects are more pronounced in ESG-sensitive industries where sustainability issues are financially material. In addition, ESG performance and long-term-oriented CEO incentives jointly promote greater investment in innovation and intangible capital, implying that ESG-aligned incentive structures shape executives’ project selection and prioritization toward long-horizon, innovation-oriented initiatives. Overall, the findings indicate that the rise of ESG as a corporate priority is reshaping executive incentives toward stronger discipline, performance alignment, and sustainable value creation. Full article

In the context of intensifying environmental regulation and sustainability pressures, firms increasingly face the challenge of sustaining green innovation under uncertainty. Green innovation resilience, which is defined as a firm’s capacity to maintain green innovation momentum and adaptively evolve technological capabilities amidst uncertainty, represents a critical organizational competence. Moving beyond static output measures, this resilience captures the intertemporal stability of firms’ green patenting activities during turbulent periods. From a systems perspective, executive compensation arrangements represent an important internal incentive mechanism that interacts with managerial characteristics and external policy environments. This study investigates how executive compensation stickiness—defined as asymmetric pay adjustment in response to firm performance—affects green innovation resilience. Using panel data from Chinese A-share-listed firms, we find that executive compensation stickiness significantly promotes green innovation resilience at the 5% level, suggesting that downward pay rigidity mitigates managerial risk aversion and supports tolerance for short-term setbacks in long-horizon green innovation. Furthermore, this positive relationship is further strengthened when executives possess environmental backgrounds (at the 5% level) and when firms receive government green innovation subsidies (at the 10% level), highlighting the interactive role of individual-level attributes and institutional policy support. Overall, the findings demonstrate how incentive asymmetry functions as a systemic property shaping firms’ adaptive responses and contribute to a broader understanding of green innovation resilience in complex socio-technical systems. Full article

The transition toward sustainable energy in Arctic and subarctic regions requires innovative approaches that account for both the unique geographical conditions and the economic and policy challenges associated with isolated power systems. This study examines how net energy metering (NEM) and net billing schemes influence distributed solar photovoltaic (PV) adoption and financial performance among utilities in Alaska’s Railbelt. The Railbelt, which supplies power to three-quarters of the state’s population, remains heavily reliant on natural gas and exhibits limited renewable penetration compared to other arctic regions. Using a stochastic risk-based modeling framework with Monte Carlo simulations and the Bass diffusion model, the analysis estimates the 15-year financial impacts of different NEM adoption scenarios on utilities. Results show that while NEM drives PV adoption through higher compensation for exported generation, it also increases potential revenue losses for utilities compared to net billing. Policy innovations like those introduced in Alaska’s House Bill 164 (HB 164), which establishes a reimbursement fund to mitigate utility revenue losses, indicate that regulatory work is being designed to balance distributed generation incentives with economic sustainability. This work provides a baseline for understanding how a policy framework influences both utility and consumer economics in terms of NEM and solar PV adoption in Arctic and subarctic systems. Full article

Residential energy renovation remains a central pillar of climate mitigation and social sustainability strategies, yet renovation rates persistently lag behind policy targets, particularly in older urban neighborhoods. This study investigates the underlying causes of renovation inertia using a neighborhood-scale mixed-methods approach that combines door-to-door household surveys, façade infrared thermography, and expert focus groups. Using a post-industrial residential district in Liège, Belgium, as an exploratory case, the study jointly analyzes building conditions, household characteristics, and renovation contexts. The results reveal that renovation failure cannot be explained solely by technical deficiencies. Instead, three interacting socio-technical mechanisms emerge: adaptive occupant behaviors that mask poor building performance, a constrained renovation agency shaped by tenure and income asymmetries, and the stratification of energy awareness along social lines. Together, these mechanisms reinforce a form of renovation lock-in in which technical degradation, behavioral adaptation, and institutional fragmentation mutually sustain inaction. By integrating physical diagnostics with social and experiential data, the study explains why conventional incentive-based renovation policies systematically underperform in comparable urban contexts. Rather than treating energy renovation as a purely technical or economic decision, the findings highlight the need for policy instruments that explicitly address agency constraints, behavioral compensation, and unequal exposure to energy-related risks. The proposed mixed-method framework is transferable to other urban neighborhoods and offers a replicable approach for diagnosing renovation barriers, supporting more socially sustainable energy transition strategies. Full article