This study investigates the relationship between corporate digital innovation and Environmental, Social, and Governance (ESG) performance, with a specific focus on the mediating role of Generative artificial intelligence technology adoption. Using a comprehensive panel dataset of 8,000 observations from the CMARS and WIND database spanning from 2015 to 2023, we employ multiple econometric techniques to examine this relationship. Our findings reveal that digital innovation significantly enhances corporate ESG performance, with GAI technology adoption serving as a crucial mediating mechanism. Specifically, digital innovation positively influences GAI technology adoption, which subsequently improves ESG performance. Furthermore, our heterogeneity analysis indicates that this relationship varies across firm size, industry type, and ownership structure. Finally, our results remain robust after addressing potential endogeneity concerns through instrumental variable estimation, propensity score matching, and differenc in differences approaches. This research contributes to the growing literature on technologydriven sustainability transformations and offers practical implications for corporate strategy and policy development in promoting sustainable business practices through technological advancement.

This paper identifies and analyzes six key strategies used to exploit the Eurosystem's financial mechanisms, and attempts a quantitative reconstruction: inflating TARGET balances, leveraging collateral swaps followed by defaults, diluting self-imposed regulatory rules, issuing money through Emergency Liquidity Assistance (ELA), acquisitions facilitated via the Agreement on Net Financial Assets (ANFA), and the perpetual (re)issuance of sovereign bonds as collateral. The paper argues that these practices stem from systemic vulnerabilities or deliberate opportunism within the Eurosystem. While it does not advocate for illicit activities, the paper highlights significant weaknesses in the current structure and concludes that comprehensive reforms are urgently needed.

This paper explores how ethical consumption can transform democratic governance toward sustainability by challenging traditional economic models centered on utility and efficiency. As societal values shift toward transparency equity and environmental responsibility ethical consumers increasingly influence markets. Drawing on Whites Kantian economic framework and Ingleharts theory of value change the paper proposes a model integrating moral imperatives into economic theory. Using a vector bundle approach it captures evolving ethical preferences advocating for an inclusive sustainability focused economic paradigm aligned with post materialist values.

Generative Pre-trained Transformers (GPTs), particularly Large Language Models (LLMs) like ChatGPT, have proven effective in content generation and productivity enhancement. However, legal risks associated with these tools lead to adoption variance and concealment of AI use within organizations. This study examines the impact of disclosure on ChatGPT adoption in legal, audit and advisory roles in consulting firms through the lens of agency theory. We conducted a survey experiment to evaluate agency costs in the context of unregulated corporate use of ChatGPT, with a particular focus on how mandatory disclosure influences information asymmetry and misaligned interests. Our findings indicate that in the absence of corporate regulations, such as an AI policy, firms may incur agency costs, which can hinder the full benefits of GPT adoption. While disclosure policies reduce information asymmetry, they do not significantly lower overall agency costs due to managers undervaluing analysts' contributions with GPT use. Finally, we examine the scope of existing regulations in Europe and the United States regarding disclosure requirements, explore the sharing of risk and responsibility within firms, and analyze how incentive mechanisms promote responsible AI adoption.

We investigate the impact of the policy-driven expansion of the diesel and renewable diesel industry on local soybean prices. Because soybean oil is a key feedstock for biodiesel and renewable diesel, significant investments have been made in new soybean crush facilities and the expansion of existing ones. We quantify the effect of both new and existing soybean plants on soybean basis using panel data and a differences-in-difference approach. The data available on new plants does not allow us to identify any statistically significant impacts. However, existing plants increase the basis by 23.36 to 9.20 cents per bushel, with the effect diminishing with distance. These results suggest the relevance of biofuel policies in supporting rural economies and have relevant policy implications.

Removing carbon dioxide from the atmosphere may slow climate change and ocean acidification. My approach converts atmospheric carbon dioxide into graphite (CD2G). The net profit for this conversion is ~$381/ton CO2 removed from the atmosphere. At the gigaton scale, CD2G factories will increase the affordability and availability of graphite. Since graphite can be used to make thermal batteries and electrodes for fuel cells and batteries, CD2G factories will help lower the cost of storing renewable energy, which will accelerate the transition to renewable energy. Replacing fossil fuel energy with renewable energy will slow the release of carbon dioxide to the atmosphere, also slowing climate change. Converting atmospheric carbon dioxide into graphite will both generate a profit and slow climate change.

Convex Hull (CH) pricing, used in US electricity markets and raising interest in Europe, is a pricing rule designed to handle markets with non-convexities such as startup costs and minimum up and down times. In such markets, the market operator makes side payments to generators to cover lost opportunity costs, and CH prices minimize the total "lost opportunity costs", which include both actual losses and missed profit opportunities. These prices can also be obtained by solving a (partial) Lagrangian dual of the original mixed-integer program, where power balance constraints are dualized. Computing CH prices then amounts to minimizing a sum of nonsmooth convex objective functions, where each term depends only on a single generator. The subgradient of each of those terms can be obtained independently by solving smaller mixed-integer programs. In this work, we benchmark a large panel of first-order methods to solve the above dual CH pricing problem. We test several dual methods, most of which not previously considered for CH pricing, namely a proximal variant of the bundle level method, subgradient methods with three different stepsize strategies, two recent parameter-free methods and an accelerated gradient method combined with smoothing. We compare those methods on two representative sets of real-world large-scale instances and complement the comparison with a (Dantzig-Wolfe) primal column generation method shown to be efficient at computing CH prices, for reference. Our numerical experiments show that the bundle proximal level method and two variants of the subgradient method perform the best among all dual methods and compare favorably with the Dantzig-Wolfe primal method.