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Real estate investment decisions are shaped by a complex environment of macroeconomic, demographic, and structural factors, where traditional linear assessment methods often fail to capture nonlinear relationships influencing aggregate housing market performance. The aim of this article is to develop a data-driven artificial intelligence framework for assessing how macroeconomic and structural conditions influence aggregate housing market performance and for providing a conceptual basis for evaluating real estate investment strategies under different economic contexts. The study uses machine learning algorithms that allow for modeling complex relationships between investment return indicators and key macroeconomic factors, such as economic growth rates, price dynamics, population concentration, and long-term structural changes. Unlike traditional econometric methods, the proposed approach identifies nonlinear and regime-dependent relationships between macroeconomic conditions and housing market performance, providing insights that can support the interpretation of different investment strategies. The results show that the factors determining investment returns are not universal, and their significance depends on the broader economic regime and market structure. This allows us to examine how changing macroeconomic conditions influence aggregate housing market performance and to discuss the potential implications for different investment strategies. The study contributes by proposing an artificial intelligence-based methodological framework that combines predictive modelling with explainable AI to support the analysis of macroeconomic influences on housing markets and to inform strategic real estate investment decision-making within complex socioeconomic systems. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. Full article

Natural gas production from mature fields is progressively shifting toward low-rate wells operating near their economic limit, creating challenges for long-term asset management. This study proposes an integrated stochastic decision-support framework combining Arps decline curve analysis, a calibrated Schwartz Type-I mean-reverting jump-diffusion model, Monte Carlo simulation, and Bellman dynamic programming to optimize marginal dry gas well management. The framework evaluates pipeline commercialization and a hybrid strategy integrating on-site electricity generation, while incorporating monetized environmental externalities associated with CO₂ emissions from gas combustion and potential post-abandonment CH₄ emissions. Application to the Mareógrafo 100 well in Mexico shows that the environmentally adjusted Bellman policy yields a mean NPV of USD 34.84 thousand, exceeding the comparable pipeline-only and hybrid strategies. Internalizing environmental costs reduces the mean optimal NPV by 46.6% relative to the economic-only formulation, while the mean abandonment time is approximately 200 days. Sensitivity analysis identifies electricity price, natural gas price, and pipeline distance as the dominant profitability drivers. The proposed framework provides a transferable methodology for jointly evaluating commercialization, environmental externalities, and abandonment decisions in mature dry gas fields under uncertainty. Full article

As data centers become major and schedulable loads of the new power system, connecting them to multiple microgrids offers a promising route to absorb local renewable energy through cross-domain coordination. However, when the microgrids belong to competing operators, coordinated scheduling forces each party to disclose its data-center load curve, storage state, and pricing strategy, which constitutes a core operational secret that no microgrid is willing to reveal. This paper develops a secure and privacy-preserving distributed scheduling scheme for data-center-integrated microgrids built on blockchain. A “data-stays-local, energy-crosses-centers” model is established that elevates privacy from an add-on feature to a first-order architectural constraint, defining a “three-no” principle and a two-layer architecture in which each microgrid optimizes its interior in plaintext and exposes only encrypted matchable factors. On this basis, a decentralized ciphertext scheduling-negotiation algorithm is designed on blockchain smart contracts, performing cross-microgrid matching under secure multi-party computation entirely in the encrypted domain, committing auditable encrypted digests on-chain, and dynamically allocating scheduling priority through an on-chain reputation mechanism. Case studies on a cluster of interconnected microgrids show that the proposed scheme attains cost and renewable accommodation within about three-tenths of a percent of the centralized optimum while reducing operational data-leakage risk from 96.7 percent to 3.8 percent, at the manageable expense of a few seconds of negotiation latency. Benchmarking against an exact mixed-integer solver on small-scale systems bounds the mean optimality gap of the decomposed scheme at 0.74 percent, with a worst case of 2.54 percent over sixty instances. Full article

Escalating energy costs and peak power demand charges pose significant challenges to the manufacturing sector. In response, industries are increasingly adopting on-site renewable energy sources and Battery Energy Storage Systems (BESSs). However, maximizing their economic benefit requires sophisticated control strategies that integrate energy management with production operations. This paper proposes and evaluates an integrated and adaptive rule-based coordination framework for BESS and machine-level switch-off policies in a production environment. Using discrete-event simulation, we model a four-machine manufacturing flow line powered by the grid and an on-site solar PV plant. We compare six distinct control policies, ranging from a benchmark case without storage to progressively more integrated context-aware strategies that incorporate price-aware BESS charging, dynamic peak-shaving, and adaptive machine switch-offs. The results demonstrate that integrated policies yield substantial economic benefits. The most advanced policy dynamically coordinates BESS dispatch with machine-level switch-off decisions based on electricity prices, production conditions, and energy availability, achieving the largest reduction in total energy costs and peak grid demand among the evaluated policies. This study quantifies the synergistic effects of combining supply-side (BESS) and demand-side (switch-off) strategies, providing a framework for developing resilient and cost-effective energy management systems in modern manufacturing. Full article

Urban regeneration has become a key strategy to address urban decline, infrastructure aging and socio-economic challenges, aiming to enhance quality of life and stimulate local development. Beyond physical renewal, it involves social, cultural and economic dimensions that contribute to the revitalization of communities. Recent literature frames regeneration as an adaptive and multifaceted process, drawing on concepts such as change management, dynamic capabilities and sensemaking to interpret urban transformation as a form of institutional learning. This study adopts a qualitative case study approach focused on the Libertà district in Bari (Italy), using document analysis, field observation, and stakeholder mapping to investigate both tangible and intangible outcomes of regeneration policies. Particular attention is devoted to understanding how socio-institutional and spatial transformations are translated into economic outcomes, using residential property values as an exploratory indicator of economic change. In this regard, variations in residential property values are considered as an exploratory indicator of economic dynamics associated with regeneration, while acknowledging that property price increases do not necessarily correspond to benefits for all community members and may also reflect issues related to affordability and social displacement. The findings suggest that regeneration should be understood as a coordinated and collaborative process involving multiple stakeholders and institutional actors. The analysis highlights the relevance of cultural initiatives, stakeholder interactions and changes in the physical environment as key elements of the transformation process. Furthermore, the study discusses the extent to which changes in the local real estate market may be associated with broader regeneration dynamics, recognizing the limitations of establishing direct causal relationships. Overall, the research proposes an original perspective on urban regeneration as a learning-based and organizational process in which social, institutional, and spatial transformations interact with economic change in complex and context-dependent ways. Full article

Grounded in the theoretical frameworks of safe haven and hedging asset theory, alongside wavelet-based time-frequency analysis, this study investigates the dynamic interconnectedness between three major cryptocurrencies and six global stock markets spanning both developed and emerging economies. Unlike prior wavelet studies that rely predominantly on graphical interpretation, this paper advances the literature by complementing graphical outputs with numerical results, offering a more rigorous and reproducible analytical foundation. Using daily price data from January 2018 to October 2024, the study applies both univariate and multivariate wavelet techniques to capture return co-movements across multiple time horizons. The univariate analysis reveals significant variance in stock returns concentrated at high frequencies, particularly over 2–4-day cycles, with pronounced fluctuations during the COVID-19 pandemic. In emerging markets such as Nigeria, additional volatility is attributed to political instability and macroeconomic crises. The multivariate analysis further demonstrates that observed co-movements between cryptocurrencies and stock markets are largely driven by interdependence rather than contagion. The paper’s findings are relevant to portfolio diversification strategies across both developed and emerging markets for investors combining stock and cryptocurrency assets. Full article

This paper investigates platform promotional subsidy strategies under price regulation. We construct a dynamic game model of incomplete information. The model involves a regulator, a monopoly platform, and heterogeneous consumers. The regulator sets a subsidy cap. The platform chooses the price and subsidy after observing its cost type. Consumers decide whether to purchase based on their valuation and the perceived subsidy value. We solve the game by backward induction. We characterize the perfect Bayesian equilibria. We derive closed-form solutions for the critical subsidy threshold, separating/self-selection equilibrium conditions, and optimal regulatory policies. The analysis yields three main findings. First, the sign of the net social benefit of the promotional subsidy determines the optimal policy. A positive sign calls for a high subsidy cap. This induces full market coverage. A negative sign calls for a ban on subsidies. Second, when the subsidy cap lies between the two platform types’ critical thresholds, a natural separating/self-selection equilibrium emerges. It operates at no cost. The feasible interval widens linearly in the cost gap. Third, diseconomies of scale have a stronger marginal effect on the critical subsidy than marginal cost. There exists an endogenously determined critical proportion of high-value consumers, which depends on the model parameters, such as $v_H,v_L,c_I,{\eta }_I$. This proportion divides the policy space into two regimes. These results provide a basis for low-cost information screening and differentiated regulation. Full article

The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends. Full article

The present research investigates the relationship between urban regeneration initiatives and residential real estate market dynamics by assessing market price appreciation associated with the factors most commonly considered in housing transactions. The study focuses on the Esquilino district in the city of Rome (Italy) with particular attention to the redevelopment of Piazza dei Cinquecento, the major public space located in front of Roma Termini railway station. The intervention aims to improve urban accessibility, reduce traffic congestion, and enhance public space quality through a new spatial configuration and the creation of a tree-lined area. The objective of the study is to verify whether, and to what extent, the ongoing regeneration project has influenced residential property values. To achieve this goal, an econometric analysis is implemented to quantify the contribution of different housing and locational attributes to residential asking prices and to identify the variables that significantly affect value formation within the local market. Given that the initiative is still in progress and approaching completion, the analysis adopts a diachronic perspective by comparing two distinct temporal stages: the ante project phase (second half of 2021) and the in itinere phase (first half of 2025). Building on the findings of a previous pre-intervention study, the research systematically examines changes in market behaviors over time, with the dual purpose of identifying variations in price determinants and analyzing the associations between the current urban transformations and the residential real estate market. The results indicate a substantial stability in the main determinants of residential property prices across the two periods, suggesting that the regeneration initiative has not yet been fully capitalized into market behaviors. However, variations in the contribution and functional relationships of some spatial variables highlight preliminary signs of market adjustment during the ongoing transformation process. The study highlights the importance of monitoring for assessing how urban regeneration processes are progressively incorporated into real estate market dynamics. The proposed framework provides a transferable tool for evaluating regeneration processes in different urban contexts, supporting evidence-based decision-making and the comparative assessment of urban transformation strategies. Full article

Addressing global climate change and China’s dual-carbon goals, advancing near-zero-carbon substations is imperative. However, existing studies often isolate carbon accounting, economic evaluation, and market mechanisms, particularly lacking analysis on the economic feasibility of multi-technology combinations. To fill this research gap, the primary objective of this paper is to establish a comprehensive systematic framework that integrates life-cycle emission accounting, life-cycle cost (LCC)–benefit evaluation, and carbon market dynamics. This framework is specifically designed to achieve two interrelated goals: (1) identification of the optimal multi-technology portfolio that balances high abatement rates with economic viability for a typical 110 kV substation; (2) determining the most economically feasible pathway toward life-cycle carbon neutrality under current carbon pricing. Based on life-cycle emission accounting, the operation stage is identified as the primary source. A technology library covering direct/indirect reductions and carbon sinks is built with LCC–benefit models. Four scenarios (S1–S4), following the “source control” to “smart management” logic, are designed to assess abatement rates, unit costs, and comprehensive performance. Introducing the carbon market mechanism, neutrality costs, and break-even points are evaluated. Results show S1 (clean air GIS + envelope optimization) achieves the best performance with a unit cost of 132.6 CNY/tCO₂e and a 36.36% reduction rate. As complexity increases, marginal abatement costs rise sharply while economic efficiency declines. Under current carbon prices, the “S1 reduction + allowance purchase” strategy is the most economical path toward life-cycle neutrality. This study provides quantitative support for technology selection and neutrality pathway planning. Full article

Uncoordinated charging and discharging of large-scale electric vehicles (EVs) exacerbates grid peak–valley fluctuations, while deep discharging accelerates battery degradation. To address these challenges, this study proposes a coordinated charging–discharging optimization strategy integrating dynamic discharge restraint and a three-dimensional weighted comprehensive objective covering electricity price signals, grid operational constraints and battery health state. First, an EV travel behavior model is established to characterize spatiotemporal availability. Subsequently, a coupled battery aging model is developed by combining a power-law-based cycle aging formulation with a square-root calendar aging model, based on which an adaptive linkage mechanism between the depth-of-discharge upper bound and a net-revenue threshold is introduced. Building on these components, this model is constructed to jointly optimize three sub-objectives: charging station revenue maximization, battery lifetime cost minimization, and load fluctuation suppression, thereby mitigating grid peak–valley differences while reducing battery degradation and discharge costs. Multi-scenario simulations demonstrate that the proposed strategy, by coupling discharge restraint with spatiotemporal dynamic pricing, enables precise peak shaving of discharge power. For a fleet of 50 EVs, the charging station revenue reaches 1073.7 CNY, the grid peak–valley difference is reduced by 9.8%, and the battery degradation cost decreases by 23.1% compared with conventional strategies, corresponding to a carbon emission reduction of 386.4 tCO₂. When scaled to 100 EVs, the revenue increases by 101.9%, while the peak–valley difference is further reduced by 0.6%, demonstrating the effectiveness of the proposed strategy in enhancing economic performance, extending battery lifetime, and supporting grid stability. Full article

In the burgeoning live stream landscape, manufacturers face critical dilemmas regarding strategic channel governance and how intermediaries’ bargaining ability dictates sustainable cooperation under Stackelberg leadership change. Addressing these issues is vital, as misaligned governance risks severe profit losses, yet standard heuristics fail to capture how leadership shifts disrupt channel coordination and model selection. To fill this gap, we model a manufacturer’s self-operated (Model SO) live stream channel and an intermediary-operated (Model IO) live stream channel to evaluate trade-offs among bargaining ability, operational costs, and spillover effects. Key findings show that in Model SO, spillover and price adjustments transform the live stream channel into a traffic-channeling gateway in which rising operational costs paradoxically boost total profits. In Model IO, bargaining ability serves as a key determinant, as high pit fees weaponize this ability for predatory commission-squeezing, while low pit fees redirect it toward volume expansion, transforming the intermediary into a synergistic partner. Furthermore, bargaining ability shifts pricing from intermediary-introduction to profit-recapture strategies while exerting cost-magnification and revenue-magnification effects under varying pit fees. Crucially, we uncover two Pareto-optimal cooperation zones alongside a non-cooperation zone caused by incentive incompatibility under mid-tier bargaining ability. Extensions show that intensified price competition turns dominant intermediaries into welfare killers and high service sensitivity induces an over-service trap, though popularity cost-sharing contracts restore coordination. Overall, this study fills a crucial analytical gap by establishing precise theoretical boundaries for channel governance, bargaining ability dynamics, and cost-driven functional transformations in live stream supply chains. Full article

The transport sector has emerged as a significant source of greenhouse gases and airborne pollutants. However, few studies have carried out a comprehensive assessment of the synergistic benefits between air pollutant and carbon emission reductions that account for the unique regional characteristics of individual provinces. In this study, we establish nonlinear prediction models for future activity levels of on-road mobile sources by integrating economic and demographic drivers. We then dynamically quantify the co-benefits of simultaneous air pollutant and CO₂ abatement under diverse mitigation scenarios. Environmental tax rates and carbon trading prices are combined with conventional elasticity coefficients and coordinate-based methodologies to convert emission cuts into economic advantages. The results show that under the most optimistic scenario, 47.86% of the CO₂ emissions could be mitigated, while emissions of NO_X may increase by 60.49% in the absence of mitigation strategies (BAU scenario). Marginal CO₂ emissions under the ELC scenario indicates a peak around 2027. Elasticity coefficients for all pollutants gradually converge toward 1, indicating that more stringent mitigation efforts enhance co-benefits. Findings in this study could provide essential insights for the co-management of CO₂ and air pollutants from road mobile sources in Guangxi and other key regions along the Belt and Road Initiative. Full article

This study examines the impact of the Russian–Ukrainian war on global financial stability, focusing on currency crises, exchange-rate dynamics, and economic vulnerability during 2003–2024 with an outlook for subsequent years. The objective is to assess how geopolitical shocks, combined with global monetary tightening, influenced the frequency and intensity of currency crises across developed and emerging economies. The study applies a quantitative comparative methodology based on a modified Exchange Market Pressure Index (EMPI) using monthly IMF data on exchange rates, reserves, interest rates, and depreciation dynamics. Currency crises are identified through threshold-based criteria, enabling cross-country and temporal comparison. A conceptual framework explains how geopolitical risk affects currency markets, financial stability, and macroeconomic performance. The findings show that crisis episodes were more frequently concentrated around the Great Recession, the COVID-19 pandemic, and the Russian–Ukrainian war. Emerging economies were more vulnerable, experiencing stronger capital outflows, sharper currency depreciation, and more frequent crises, while developed economies were affected mainly through inflation and energy price shocks. The war intensified financial fragmentation, increased safe-haven flows toward the US dollar, gold, and Swiss franc, and raised systemic risks in debt, banking, and corporate sectors. The study concludes that differentiated macroeconomic strategies, stronger external buffers, and enhanced international financial coordination are necessary to reduce risks and preserve currency stability. Full article