Decision support systems like computer-aided energy system analysis (ESA) are considered one of the main pillars for developing sustainable and reliable energy transformation strategies. Although today's diverse tools can already support decision-makers in a variety of research questions, further developments are still necessary. Intending to identify opportunities and challenges in the field, we classify modelling capabilities (32), methodologies (15) implementation issues (15) and management issues (7) from an extensive literature review. Based on a quantitative expert survey of energy system modellers (N=61) mainly working with simulation and optimisation models, the status of development and the complexity of realisation of those modelling topics are assessed. While the rated items are considered to be more complex than actually represented, no significant outliers are determinable, showing that there is no consensus about particular aspects of ESA that are lacking development. Nevertheless, a classification of the items in terms of a specially defined modelling strategy matrix identifies capabilities like land-use planning patterns, equity and distributional effects and endogenous technological learning as "low hanging fruits" for enhancement, as well as a large number of complex topics that are already well implemented. The remaining "tough nuts" regarding modelling capabilities include non-energy sector and social behaviour interaction effects. In general, the optimisation and simulation models differ in their respective strengths, justifying the existence of both. While methods were generally rated as quite well developed, combinatorial optimisation approaches, as well as machine learning, are identified as important research methods to be developed further for ESA.

We propose and estimate a model of demand and supply of annuities. To this end, we use rich data from Chile, where annuities are bought and sold in a private market via a two-stage process: first-price auctions followed by bargaining. We model firms with private information about costs and retirees with different mortalities and preferences for bequests and firms' risk ratings. We find substantial costs and preference heterogeneity, and because there are many firms, the market performs well. Counterfactuals show that simplifying the current mechanism with English auctions and "shutting down" risk ratings increase pensions, but only for high-savers.

Environmental, social, and governance (ESG) scores measure companies' activities concerning sustainability and societal impact and are organized on three pillars: Environmental (E-), Social (S-), and Governance (G-). Different approaches have been proposed to compute ESG scores for companies, which typically rely on the aggregation of many and different sources of information. These complementary non-financial ESG scores should provide information about the ESG performance and risks of different companies. However, the extent of missing information makes the reliability of ESG scores questionable. To account for the missing information in the underlying ESG pillars, we introduce a new pillar, the so-called Missing (M-) pillar, and propose an optimization approach to compute new ESG (ESGM) scores, which should also be related to the company riskiness. As a result, the ESGM scores allow for incorporating the extent of missing information and establishing some meaningful relationship with respect to the riskiness of the companies under consideration. Interesting insights into the current limitations of ESG scoring methodology are discussed.

In this paper we deal with the optimal bankruptcy problem for an agent who can optimally allocate her consumption rate, the amount of capital invested in the risky asset as well as her leisure time. In our framework, the agent is endowed by an initial debt, and she is required to repay her debt continuously. Declaring bankruptcy, the debt repayment is exempted at the cost of a wealth shrinkage. We implement the duality method to solve the problem analytically and conduct a sensitivity analysis to the cost and benefit parameters of bankruptcy. Introducing the flexible leisure/working rate, and therefore the labour income, into the bankruptcy model, we investigate its effect on the optimal strategies.

We propose an empirical framework for Cournot oligopoly with private information about costs. First, considering a linear demand with a random intercept, we characterize the Bayesian Cournot-Nash equilibrium and determine its testable implications. Then we establish nonparametric identification of the joint distribution of demand and technology shock and firm-specific cost distributions. Finally, we propose a likelihood-based estimation method and apply it to the global crude oil market. Using counterfactuals, we also quantify the effect of firms sharing information about their costs on consumer welfare. We also extend the model to include either firm-specific conduct parameters, nonlinear demand, or selective entry.

Onshore wind development has historically focused on cost-efficiency, which may lead to inequitable turbine distributions and public resistance due to landscape impacts. Using a multi-criteria planning approach, we show how onshore wind capacity targets can be achieved by 2050 in a cost-efficient, equitable and publicly acceptable way. For the case study of Germany, we build on the existing turbine stock and use open data on technically feasible turbine locations and scenicness of landscapes to plan the optimal expansion. The analysis shows that while the trade-off between cost-efficiency and public acceptance is rather weak with about 15% higher costs or scenicness, an equitable distribution has a large impact on these criteria. Although the onshore wind capacity per inhabitant could be distributed about 220% more equitably through the expansion, equity would severely limit planning flexibility by 2050. Our analysis assists stakeholders in resolving the onshore wind expansion trilemma.

In this work, we study the numerical approximation of a class of singular fully coupled forward backward stochastic differential equations. These equations have a degenerate forward component and non-smooth terminal condition. They are used, for example, in the modeling of carbon market[9] and are linked to scalar conservation law perturbed by a diffusion. Classical FBSDEs methods fail to capture the correct entropy solution to the associated quasi-linear PDE. We introduce a splitting approach that circumvent this difficulty by treating differently the numerical approximation of the diffusion part and the non-linear transport part. Under the structural condition guaranteeing the well-posedness of the singular FBSDEs [8], we show that the splitting method is convergent with a rate $1/2$. We implement the splitting scheme combining non-linear regression based on deep neural networks and conservative finite difference schemes. The numerical tests show very good results in possibly high dimensional framework.

Policymakers decide on alternative policies facing restricted budgets and uncertain, ever-changing future. Designing housing policies is further difficult given the heterogeneous characteristics of properties themselves and the intricacy of housing markets and the spatial context of cities. We propose PolicySpace2 (PS2) as an adapted and extended version of the open source PolicySpace agent-based model. PS2 is a computer simulation that relies on empirically detailed spatial data to model real estate, along with labor, credit, and goods and services markets. Interaction among workers, firms, a bank, households and municipalities follow the literature benchmarks to integrate economic, spatial and transport literature. PS2 is applied to a comparison among three competing municipal housing policies aimed at alleviating poverty: (a) property acquisition and distribution, (b) rental vouchers, and (c) monetary aid. Within the model context, the monetary aid, that is, smaller amounts of help for a larger number of households, makes the economy perform better in terms of production, consumption, reduction of inequality, and maintenance of financial duties. PS2 as such is also a framework that may be further adapted to a number of related research questions.

On-demand ridepooling (ODRP) can become a powerful alternative to reduce congestion and emissions, if it attracts private car users rather than from public transport. Therefore, it is crucial to identify the strategic phenomena that determine when ODRP systems can run efficiently, and understand when they could be integrated into a public transport network. In this paper, we analyze the performance of an ODRP system operated in a zone covered by a single public transport line. The fleet of low-capacity vehicles is endogenously adapted to the demand. Considering both users' and operators' costs, we identify two sources of scale economies: when demand grows, the average cost is reduced due to an equivalent of the Mohring Effect (that is present in public transport), and due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect. A counter-balance force, called Flex-route Effect, is observed when the vehicle loads increase and users face longer detours. We find a specific demand range in which this last effect dominates the others, imposing diseconomies of scale when only users' costs are considered. Such a phenomenon is not observed in public transport systems based on fixed routes. However, when considering both users' and operators' costs, scale economies prevail. We compare the ODRP results against public transport, for a feeder line and a circular line with homogeneous demand. We show that ODRP is more competitive when users share a common destination (the feeder line) and when the demand is low, although scale effects suggest that ODRP can also play a role when the demand is high. Relaxing door-to-door vehicle requirements to allow short walks, is shown to be crucial for ODRP to become a viable alternative for both human-driven and automated vehicles, if the ODRP must serve all requests.

Models on innovation, for the most part, do not include a comprehensive and end-to-end view. Most innovation policy attention seems to be focused on the capacity to innovate and on input factors such as R&D investment, scientific institutions, human resources and capital. Such inputs frequently serve as proxies for innovativeness and are correlated with intermediate outputs such as patent counts and outcomes such as GDP per capita. While this kind of analysis is generally indicative of innovative behaviour, it is less useful in terms of discriminating causality and what drives successful strategy or public policy interventions. This situation has led to the developing of new frameworks for the innovation system led by National Science and Technology Policy Centres across the globe. These new models of innovation are variously referred to as the National Innovation Ecosystem. There is, however, a fundamental question that needs to be answered: what elements should an innovation policy include, and how should such policies be implemented? This paper attempts to answer this question.

The purpose of this article is to introduce a new L\'evy process, termed Variance Gamma++ process, to model the dynamic of assets in illiquid markets. Such a process has the mathematical tractability of the Variance Gamma process and is obtained applying the self-decomposability of the gamma law. Compared to the Variance Gamma model, it has an additional parameter representing the measure of the trading activity. We give a full characterization of the Variance Gamma++ process in terms of its characteristic triplet, characteristic function and transition density. In addition, we provide efficient path simulation algorithms, both forward and backward in time. We also obtain an efficient "integral-free" explicit pricing formula for European options. These results are instrumental to apply Fourier-based option pricing and maximum likelihood techniques for the parameter estimation. Finally, we apply our model to illiquid markets, namely to the calibration of European power future market data. We accordingly evaluate exotic derivatives using the Monte Carlo method and compare these values to those obtained using the Variance Gamma process and give an economic interpretation of the obtained results. Finally, we illustrate an extension to the multivariate framework.

According to common understanding, in free completion of a private product, market and price, the two main factors in the competition that leads to economic efficiency, always exist together. This paper, however, points out the phenomenon that in some free competitions the two factors are separated hence causing inefficiency. For one type, the market exists whereas the price is absent, i.e. free, for a product. An example of this type is the job application market where the problem of over-application commonly exists, costing recruiters much time in finding desired candidates from massive applicants, resulting in inefficiency. To solve the problem, this paper proposes a solution that the recruiters charge submission fees to the applications to make the competition complete with both factors, hence enhancing the efficiency. For the other type, the price exists whereas the market is absent for a product. An example of this type is the real estate agent market, where the price of the agents exists but the market, i.e. the facility allowing the sellers' information to be efficiently discovered, is largely absent, also causing inefficiency. In summary, the contribution of this paper consists of two aspects: one is the discovery of the possible separation of the two factors in free competitions; the other is, thanks to the discovery, a solution to the over-application problem in the job market.

Women bear a disproportionate burden of unpaid household and care work across the world and especially in South Asia. However, due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been made in the context of India. The present paper, using the recent Time Use Survey (2019) data addresses two concerns. First, it examines the socioeconomic and demographic factors associated with variation in the time spent on unpaid household and care work among men and women. Second, it analyses how much of the gender gap in the time allocated to unpaid work can be explained by the difference in socioeconomic and demographic factors of men and women by employing the Oaxaca-Blinder technique. The findings show that women spend much higher time than men in unpaid household and care work. We find that marital status, employment, having additional females in the household are the most important predictors of time devoted to unpaid household work among women. The decomposition results reveal that differences in socioeconomic and demographic factors between men and women do not explain most of the gender gap in unpaid household work. Our results suggest that it is not the rational economic choices that determine the division of household chores between women and men; instead, unobserved gender norms and practices govern the allocation of unpaid work within Indian households.