Antitrust authorities frequently rely on structural divestitures to address competitive concerns raised by mergers. Using census-level establishment data and proprietary transaction records from the U.S. grocery sector, we provide systematic evidence on the long-run effects of such remedies. Divested stores experience an average 31 percent decline in employment over five years, driven by elevated exit rates and persistent contraction among surviving establishments. Sales similarly decline. Transaction-level evidence indicates that divested assets are systematically weaker and are often transferred to lower-capability buyers. These findings suggest that structural remedies may be less effective when the implementation of divestitures allows merging parties substantial discretion over the assets and buyers involved.

Volatility forecasting becomes challenging when market conditions shift and model performance varies across market states. Motivated by this instability, we develop a risk-sensitive specialist routing framework for ETF volatility forecasting. The framework uses online risk-sensitive evaluation and state-dependent gating to combine different forecasting specialists across calm and stressed market states. Using a daily panel of six ETFs under a rolling walk-forward design, we find that the strongest forecaster is regime-dependent rather than stable across all states. Relative to the rolling-best baseline, the proposed routing framework reduces high-volatility forecast loss by about 24% and underprediction loss by about 22%. These results suggest that specialist routing provides a practical forecasting architecture that adapts to changing market conditions.

We extend the Fokker-Planck framework of Froseth (2026, arXiv:2603.05283) to populations of investors with heterogeneous, persistent return-generating ability. When the drift coefficient in the Langevin equation for log-wealth varies across investors, the proportional wealth tax remains a uniform drift shift but ceases to be neutral in the economic sense: its real incidence differs across ability types, and the stationary wealth distribution changes shape. We derive the extended Fokker-Planck equation on the joint space of log-wealth and ability, characterise the conditions under which the drift-shift symmetry breaks, and identify the consequences for asset prices and portfolio allocations. The analysis connects the neutrality results of Froseth (2026, arXiv:2603.05264) and the Fokker-Planck dynamics of Froseth (2026, arXiv:2603.05283) to the heterogeneous-returns literature, notably the "use-it-or-lose-it" mechanism of Guvenen, Kambourov, Kuruscu, Ocampo-Diaz and Chen (2023).

We develop spectral portfolio theory by establishing a direct identification: neural network weight matrices trained on stochastic processes are portfolio allocation matrices, and their spectral structure encodes factor decompositions and wealth concentration patterns. The three forces governing stochastic gradient descent (SGD) - gradient signal, dimensional regularisation, and eigenvalue repulsion - translate directly into portfolio dynamics: smart money, survival constraint, and endogenous diversification. The spectral properties of SGD weight matrices transition from Marchenko-Pastur statistics (additive regime, short horizon) to inverse-Wishart via the free log-normal (multiplicative regime, long horizon), mirroring the transition from daily returns to long-run wealth compounding. We unify the cross-sectional wealth dynamics of Bouchaud and Mezard (2000), the within-portfolio dynamics of Olsen et al. (2025), and the scalar Fokker-Planck framework via a common spectral foundation. A central result is the Spectral Invariance Theorem: any isotropic perturbation to the portfolio objective preserves the singular-value distribution up to scale and shift, while anisotropic perturbations produce spectral distortion proportional to their cross-asset variance. We develop applications to portfolio design, wealth inequality measurement, tax policy, and neural network diagnostics. In the tax context, the invariance result recovers and generalises the neutrality conditions of Froseth (2026).

Post-growth has emerged as an umbrella term for various sustainability visions that advocate the pursuit of environmental sustainability, social equity, and human wellbeing, while questioning the continued pursuit of economic growth. Although there are increasing calls to include post-growth scenarios in high-level assessments, a coherent framework with what is required to model post-growth adequately remains absent. This article addresses this gap by: (1) identifying the minimum requirements for post-growth models, and (2) establishing a set of model elements for representing specific policy themes. Drawing on a survey of modellers and on relevant post-growth literature, we develop a framework of minimum requirements for post-growth modelling that integrates three spheres: biophysical, economic, and social, and links them to post-growth goals. Within the biophysical sphere, we argue that embeddedness requires the inclusion of resource use and pollution, environmental limits, and feedback mechanisms from the environment onto society. Within the economic sphere, models should disaggregate households, incorporate limits to technological change and decoupling, include different types of government interventions, and calculate GDP or output endogenously. Within the social sphere, models should represent time use, material and non-material need satisfiers, and the affordability of essential goods and services. Specific policies and transformation scenarios require additional features, such as sectoral disaggregation or representation of the financial system. Our framework guides the development of models that can simulate both post-growth and pro-growth policies and scenarios, an urgently needed tool for informing policymakers and stakeholders about the full range of options for pursuing sustainability, equity, and wellbeing.

Increases in national concentration have been a salient feature of industry dynamics in the U.S. and have contributed to concerns about increasing market power. Yet, local trends may be more informative about market power, particularly in the retail sector where consumers have traditionally shopped at nearby stores. We find that local concentration has increased almost in parallel with national concentration using novel Census data on product-level revenue for all U.S. retail stores between 1992 and 2012. The increases in concentration are broad based, affecting most markets, products, and retail industries. We show that the expansion of multi-market firms into new markets explains most of the increase in national retail concentration, with consolidation via increases in local market shares increasing in importance between 1997 and 2007, and single-market firms playing a negligible role. Finally, we find that increases in local concentration can explain one-quarter to one-third of the observed rise in retail gross margins.

The exponential growth of financial research has rendered traditional systematic literature reviews (SLRs) increasingly impractical, as manual screening and narrative synthesis struggle to keep pace with the scale and complexity of modern scholarship. While the existing artificial intelligence (AI) and natural language processing (NLP) approaches often often produce outputs that are efficient but contextually limited, still requiring substantial expert oversight. To address these challenges, we propose LR-Robot, a novel framework in which domain experts define multidimensional classification taxonomies and prompt constraints that encode conceptual boundaries, large language models (LLMs) execute scalable classification across large corpora, and systematic human-in-the-loop evaluation ensures reliability before full-dataset deployment.The framework further leverages retrieval-augmented generation (RAG) to support downstream analyses including temporal evolution tracking and label-enhanced citation networks. We demonstrate the framework on a corpus of 12,666 option pricing articles spanning 50 years, designing a four-dimensional taxonomy and systematically evaluating up to eleven mainstream LLMs across classification tasks of varying complexity. The results reveal the current capabilities of AI in understanding and synthesizing literature, uncover emerging trends, reveal structural research patterns, and highlight core research directions. By accelerating labor-intensive review stages while preserving interpretive accuracy, LR-Robot provides a practical, customizable, and high-quality approach for AI-assisted SLRs.

We leverage a quasi natural experiment from India on introduction of free bus schemes for women across five states to study it's impact on women's workforce participation. We use two rounds of the representative Time Use Survey and a triple difference estimation strategy, complemented by an event study framework to identify the causal relationship of interest. Findings reveal that the bus scheme was successful in improving women's paid work participation and duration of employment. We confirm that these results are not merely a continuation of prior trends. The scheme's effects are concentrated among early adopters like Punjab and Tamil Nadu, two states with historically different levels of women's workforce participation. We also find disproportionately higher effects for women residing in more patriarchal districts with higher mobility restrictions. We argue that the scheme works through easing of non-financial binding constraints, which lowers the barriers to women's mobility and workforce participation.

In computational paralinguistics, detecting cognitive load and deception from speech signals is a heavily researched domain. Recent efforts have attempted to apply these acoustic frameworks to corporate earnings calls to predict catastrophic stock market volatility. In this study, we empirically investigate the limits of acoustic feature extraction (pitch, jitter, and hesitation) when applied to highly trained speakers in in-the-wild teleconference environments. Utilizing a two-stream late-fusion architecture, we contrast an acoustic-based stream with a baseline Natural Language Processing (NLP) stream. The isolated NLP model achieved a recall of 66.25% for tail-risk downside events. Surprisingly, integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%. We identify this degradation as Acoustic Camouflage, where media-trained vocal regulation introduces contradictory noise that disrupts multimodal meta-learners. We present these findings as a boundary condition for speech processing applications in high-stakes financial forecasting.

We study a discrete-time multi-period portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the excess of Conditional Value-at-Risk over expected terminal wealth. The objective is to maximize expected return subject to a global tail-risk constraint, leading to a time-inconsistent precommitment problem. We propose a recurrent neural-network-based approach to approximate the optimal precommitment policy, which accommodates path-dependent risk constraints and highdimensional state dynamics without relying on dynamic programming. The explicit constraint formulation allows for exact penalty methods and provides a transparent notion of feasibility. The methodology is validated in a classical complete-market financial model and extended to a multi-period portfolio allocation problem in (re)insurance, capturing the long-term risk dynamics of insurance liabilities.

Electricity markets are changing, driven by large-scale renewable integration and rising demand from electrification and digitalisation. This raises fundamental questions about how electricity prices form as the relationships among key price determinants evolve. Here we apply causal discovery to characterise these dynamics across major supply- and demand-side drivers of wholesale electricity prices in Texas, where rapid renewable growth intersects with surging demand. We show that wind generation has become the dominant causal driver of day-ahead electricity prices with effects more than 3 times larger than those of natural gas prices, overturning the view of the Texas market as gas-price-driven. Wind reduces prices locally but redistributes congestion costs across regions in seasonally varying patterns. Natural gas prices remain causally relevant, though their influence is modest and the dominant gas benchmark changes over time. Electricity demand also shows region- and period-specific causal effects. These findings highlight the need for causal models that capture time-varying relationships across both supply and demand to guide system planners and market participants navigating the ongoing transition.

This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct a teacher student learning pipeline in which a Conditional Value at Risk (CVaR) optimizer generates supervisory labels, and neural models (Bayesian and deterministic) are trained using both real and synthetically augmented data. The synthetic data is generated using a factor based model with t copula residuals, enabling training beyond the limited real sample of 104 labeled observations. We evaluate four student models under a structured experimental framework comprising (i) controlled synthetic experiments (3 x 5 seed grid), (ii) in-distribution real market evaluation (C2A) and (iii) cross-universe generalization (D2A). In real-market settings, models are deployed using a rolling evaluation protocol where a frozen pretrained model is periodically fine tuned on recent observations and reset to its base state, ensuring stability while allowing limited adaptation. Results show that student models can match or outperform the CVaR teacher in several settings, while achieving improved robustness under regime shifts and reduced turnover. These findings suggest that hybrid optimization learning approaches can enhance portfolio construction in data constrained environments

Predicting real-world events from live market signals demands systems that fuse qualitative news with quantitative order-book dynamics under strict temporal discipline -- a challenge existing benchmarks fail to capture. We present \textbf{PolyBench}, a multimodal benchmark derived from Polymarket that records point-in-time cross-sections of 38,666 binary prediction markets spanning 4,997 events, synchronously coupling each snapshot with a Central Limit Order Book (CLOB) state and a real-time news stream. Using PolyBench, we evaluate seven state-of-the-art Large Language Models -- spanning open- and closed-source families -- generating 36,165 predictions under identical, timestamp-locked market states collected between February 6 and 12, 2026. Our multidimensional framework assesses directional accuracy, our proposed Confidence-Weighted Return (CWR), Annualized Percentage Yield (APY), and Sharpe ratio via realistic order-book execution simulation. The results reveal a pronounced performance divergence: only two of seven models achieve positive financial returns -- MiMo-V2-Flash at \textbf{17.6%} CWR and Gemini-3-Flash at 6.2% CWR -- while the remaining five incur losses despite uniformly high stated confidence. These findings highlight the gap between surface-level language fluency and genuine probabilistic reasoning under live market uncertainty, and establish PolyBench as a contamination-proof, financially-grounded evaluation standard for future LLM research. Our dataset and code available at \underline{\href{https://github.com/PolyBench/PolyBench}{https://github.com/PolyBench/PolyBench}}.

In centralized assignment problems, agents may have preferences over joint rather than individual assignments, such as couples in residency matching or siblings in school choice and daycare. Standard preference estimation methods typically ignore such complementarities. This paper develops an empirical framework that explicitly incorporates them. Using data from daycare assignment in a municipality in Japan, we estimate a model in which families incur both additional commuting distance and a fixed non-distance disutility when siblings are assigned to different facilities. We find that split assignment generates a large disutility, equivalent to more than twice the average commuting distance. We then simulate counterfactual assignment policies that vary the strength of sibling priority and evaluate welfare. The sibling priority reform that we designed and that was implemented in 2024 increases welfare by 6.4% while reducing inequality in assignment rates across sibling groups; models that ignore sibling complementarities substantially understate these gains. At the same time, we uncover a clear efficiency-equity tradeoff: along the frontier, increasing mean welfare by 100 meters is associated with an increase in inequality of about 1.7 percentage points, and the welfare-maximizing policy reverses much of the reform's reduction in inequality, largely through the displacement of households without siblings.

Decentralized finance introduces new business models and use cases as part of digital finance. Restaking has recently emerged as a transformative mechanism in DeFi, promising extra yields but introducing complex and interconnected risks. The paper monitors the current restaking landscape, empirically analyzes the revenue drivers of a liquid restaking protocol, and conducts a technical investigation on the emitted risk arising from the interconnection between liquid restaking and other protocols. The revenue dynamics of Renzo Protocol are analyzed by employing an OLS regression model, Granger-causality and random forest feature importance tests. Our results identify that revenue is primarily predicted by the value locked in the underlying EigenLayer ecosystem, the yield of Renzo protocol's liquid restaking token and the multi-blockchain expansion of that token. The multi-blockchain expansion of the liquid restaking token presents a double-edged sword: bridging to other networks is crucial for user adoption, but it adds the bridge risks to the existing risks of restaking. We investigate the cross-contamination risk between different DeFi services and the liquid restaking protocol. By mapping the asset flow across the decentralized finance ecosystem, it is detected that the bridge risk of the current size of Renzo's liquid-restaking assets does not impose a systemic risk on the current restaking and staking ecosystem. To address the potential consequences of the emphasized interconnection risks, we introduce two hypothetical scenarios and a stress test, assuming a large number of compromised liquid restaking tokens and a smart contract logic failure in a DeFi protocol. Considering the overall liquid-restaking protocols and the growing interconnection, this analysis requires further work to explore the growing complexities.

A proportional wealth tax - a levy on the stock of wealth - preserves portfolio neutrality by acting as a uniform drift shift in the Fokker-Planck equation for wealth dynamics. We extend this result to the full system of ownership taxes (eierkostnader) that a shareholder faces: a corporate tax on gross profits, a capital income tax on the risk-free return, a dividend and capital gains tax on the excess return, and a wealth tax on net assets. Each tax modifies the drift of the wealth process in a distinct way - multiplicative rescaling, constant shift, or regime-dependent compression - while leaving the diffusion coefficient unchanged. We show that the combined system preserves portfolio neutrality under three conditions: (i) the capital income tax rate equals the corporate tax rate, (ii) the shielding rate equals the risk-free rate, and (iii) the wealth tax assessment is uniform across assets. When these conditions hold, the after-tax excess return is a uniform rescaling of the pre-tax excess return by the factor $(1-τ_c)(1-τ_d)$, and the drift-shift symmetry of the wealth-tax-only case generalises to a drift-shift-and-rescale symmetry. We classify the distortions that arise when each condition fails and show that flow-tax distortions and stock-tax distortions are additively separable: they do not interact. The shielding deduction - a feature of several real-world tax systems, including the Norwegian aksjonaermodellen - emerges as the mechanism that restores the symmetry between equity and debt taxation within this framework. Calibrated to the Norwegian dual income tax, conditions (i) and (ii) hold by institutional design; the only binding distortion is non-uniform wealth tax assessment, which generates portfolio tilts roughly 300 times larger than any residual flow-tax channel.

We reformulate the neutral wealth tax framework of Froeseth (2026; arXiv:2603.05264) in the language of stochastic dynamics and statistical physics. Individual wealth under geometric Brownian motion satisfies a Langevin equation with multiplicative noise; the probability density of wealth across a population then evolves according to a Fokker-Planck equation. A proportional wealth tax at market value enters as a uniform reduction of the drift coefficient, preserving the diffusion structure and all relative probability currents. This drift-shift symmetry is the physical content of tax neutrality. Each channel through which neutrality breaks down in practice - book-value assessment, liquidity frictions, forced dividend extraction, migration, and market impact - corresponds to a specific violation of this symmetry: a state-dependent, asset-dependent, or flow-dependent modification of the Fokker-Planck equation. The framework clarifies when wealth taxation is a benign rescaling of the dynamics and when it introduces genuinely new physics.

Signaling is wasteful. But how wasteful? We study the fraction of surplus dissipated in a separating equilibrium. For isoelastic environments, this waste ratio has a simple formula: $β/(β+σ)$, where $β$ is the benefit elasticity (reward to higher perception) and $σ$ is the elasticity of higher types' relative cost advantage. The ratio is constant across types and is independent of other parameters, including convexity of cost in the signal. We show that the directional effects of $β$ and $σ$ on waste extend to non-isoelastic environments.

We propose and analyze a continuous-time robust reinforcement learning framework for optimal stopping under ambiguity. In this framework, an agent chooses a robust exploratory stopping time motivated by two objectives: robust decision-making under ambiguity and learning about the unknown environment. Here, ambiguity refers to considering multiple probability measures dominated by a reference measure, reflecting the agent's awareness that the reference measure representing her learned belief about the environment would be erroneous. Using the $g$-expectation framework, we reformulate the optimal stopping problem under ambiguity as a robust exploratory control problem with Bernoulli distributed controls. We then characterize the optimal Bernoulli distributed control via backward stochastic differential equations and, based on this, construct the robust exploratory stopping time that approximates the optimal stopping time under ambiguity. Last, we establish a policy iteration theorem and implement it as a reinforcement learning algorithm. Numerical experiments demonstrate the convergence, robustness, and scalability of our reinforcement learning algorithm across different levels of ambiguity and exploration.

A robust thermodynamic relation between inflation corrected monetary valuation and energy emerges from existing work. This is based on the energy used, the aggregate efficiency of all production processes ($Λ(t)$) in terms of Joules per dollar of gross world product, and the gross world product: $\frac{E_A(t) \text{[J]}}{Λ(t) \text{[J/\$]}} = Y(t) [\$]$ where: J = Joules, \$= currency. This directs us to the production system and all of its processes in addition to alternatives to carbon energy. The original relation appeared in 'Are there basic physical constraints on future anthropogenic emissions of carbon dioxide?' (Garrett 2011). There a foundation assumption was made that a variable $λ$ representing energy per dollar would disprove the presented model. However, because $λ$ has dimension [$\frac{E}{\$ \; GWP}$], it represents the aggregate efficiency of all global production, and cannot be a constant in an economic model. Thus, aggregate production efficiency is: $Λ(t) \equiv \sum {λ_i(t) \cdot \frac{P_i}{GWP}}$. The claimed 50 year constant relation of $W$ ($\sum_{i = n}^{t} Y_i \text{ [\$]}$) to the energy $E$ of the final year is incorrect -- the relation is not flat, nor should this be expected. The graph of $W$ from 1970 back in time is shown to have an historic minimum in 1970 driven by growth in energy consumption and increasing efficiency of energy use that is unlikely to be repeated. With improvements, a robust thermodynamic model is obtained that has general application to the relationship between money and energy and may be useable for evaluating the health of currencies and economies.

Generative AI is transforming higher education, yet systematic evidence on student adoption, usage patterns, and perceived learning impacts remains scarce. Using survey data from a selective U.S. college, we document rapid generative-AI adoption, reaching over 80 percent within two years of ChatGPT's release. Adoption varies sharply across disciplines, demographics, and achievement levels. Students use AI both to augment their learning -- by obtaining explanations and feedback -- and to automate coursework by generating final outputs, with augmentation more common than automation. Students generally perceive AI as benefiting their learning, and these beliefs are strongly correlated with adoption. Institutional policies shape usage but have uneven effects, in part because awareness and compliance vary across student groups. These findings suggest that effective AI policies must distinguish between uses that enhance learning and those that substitute for it.

The breaching of planetary boundaries and the potentially catastrophic consequences of climate change are leading researchers to question the endless pursuit of economic growth. Several macroeconomic modelling studies have now examined whether a zero-growth trajectory in a capitalist system with interest-bearing debt can be economically stable, with mixed results. However, stability has not previously been explored at the microeconomic level, where it is important to know the consequences of zero-growth on e.g., distribution of firm sizes, market instability and risk of individual firm bankruptcy. Here we address this by developing an agent-based model incorporating Minskyan financial dynamics, the Post-Growth DYNamic Agent-based MINskyan (PG-DYNAMIN) model, and carrying out simultaneous macro- and microeconomic analyses. Accounting for the fact that growing capitalist economies are unstable and produce crises, we compare the relative stability of growth and zero-growth scenarios. This is achieved by tweaking an exogenous productivity parameter. We find zero-growth scenarios are viable yet exhibit distinct dynamics from growth scenarios. Under zero-growth, GDP was less volatile, there was reduced systemic risk in the credit network, lower unemployment rates, a higher wages share of GDP for workers, lower corporate debt to GDP ratio, and a reduction in market instability. Additionally, there was a higher rate of inflation, lower profit share of GDP for firms, increased market concentration, more economic crises with higher severity, and increased default probabilities for firms during periods of crises.

Land use is a critical factor in the siting of renewable energy facilities and is often scrutinized due to perceived conflicts with other land demands. Meanwhile, substantial areas are devoted to activities such as golf, which are accessible to only a select few and have a significant land and environmental footprint. Our study shows that in countries such as the United States and the United Kingdom, far more land is allocated to golf courses than to renewable energy facilities. Areas equivalent to those currently used for golf could support the installation of up to 842 GW of solar and 659 GW of wind capacity in the top ten countries with the most golf courses. In many of these countries, this potential exceeds both current installed capacity and medium-term projections. These findings underscore the untapped potential of rethinking land use priorities to accelerate the transition to renewable energy.

We consider the problem of estimating the roughness of the volatility process in a stochastic volatility model that arises as a nonlinear function of fractional Brownian motion with drift. To this end, we introduce a new estimator that measures the so-called roughness exponent of a continuous trajectory, based on discrete observations of its antiderivative. The estimator has a very simple form and can be computed with great efficiency on large data sets. It is not derived from distributional assumptions but from strictly pathwise considerations. We provide conditions on the underlying trajectory under which our estimator converges in a strictly pathwise sense. Then we verify that these conditions are satisfied by almost every sample path of fractional Brownian motion (with drift). As a consequence, we obtain strong consistency theorems in the context of a large class of rough volatility models, such as the rough fractional volatility model and the rough Bergomi model. We also demonstrate that our estimator is robust with respect to proxy errors between the integrated and realized variance, and that it can be applied to estimate the roughness exponent directly from the price trajectory. Numerical simulations show that our estimation procedure performs well after passing to a scale-invariant modification of our estimator.

Lithium is a critical material for the energy transition, but conventional procurement methods have significant environmental impacts. In this study, we utilize regional energy system optimizations to investigate the techno-economic potential of the low-carbon alternative of direct lithium extraction in deep geothermal plants. We show that geothermal plants will become cost-competitive in conjunction with lithium extraction, even under unfavorable conditions and partially displace photovoltaics, wind power, and storage from energy systems. Our analysis indicates that if 10% of municipalities in the Upper Rhine Graben area in Germany constructed deep geothermal plants, they could provide enough lithium to produce about 1.2 million electric vehicle battery packs per year, equivalent to 70% of today`s annual electric vehicle registrations in the European Union. This approach could offer significant environmental benefits and has high potential for mass application also in other countries, such as the United States, United Kingdom, France, and Italy, highlighting the importance of further research and development of this technology.

Recent global events emphasize the importance of a reliable energy supply. One way to increase energy supply security is through decentralized off-grid renewable energy systems, for which a growing number of case studies are researched. This review gives a global overview of the levelized cost of electricity (LCOE) for these autonomous energy systems, which range from 0.03 \$_{2021}/kWh to over 1.00 \$_{2021}/kWh worldwide. The average LCOEs for 100% renewable energy systems have decreased by 9% annually between 2016 and 2021 from 0.54 \$_{2021}/kWh to 0.29 \$_{2021}/kWh, presumably due to cost reductions in renewable energy and storage technologies. Furthermore, we identify and discuss seven key reasons why LCOEs are frequently overestimated or underestimated in literature, and how this can be prevented in the future. Our overview can be employed to verify findings on off-grid systems, to assess where these systems might be deployed and how costs evolve.

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.

Real-world problems are becoming highly complex and, therefore, have to be solved with combinatorial optimisation (CO) techniques. Motivated by the strong increase of publications on CO, 8,393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organisations and authors as well as their collaborations, the most relevant CO problems, solution methods and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem-oriented studies deal particularly with real-world applications within the energy sector, production sector or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.