CS11: Energy Economics and Modeling

Climate change is a global externality that requires coordinated responses beyond isolated national action. This paper demonstrates that integrated approaches are important for assessing the economic and climate effects of public policies under nonlinear dynamics, long-term horizons, and interactions between regions. This study expands on the classic Regional Integrated model of Climate and the Economy (RICE) by incorporating energy as a factor interdependent with capital in the production function and by disaggregating Brazil from the Latin American, calibrating it using national data on GDP, population, capital stock, and emissions. It explores different types of climate cooperation and how they affect economic growth, emissions mitigation, regional and global intertemporal welfare, and the social cost of carbon (SCC). Four trajectories are simulated: (i) No climate policies; (ii) Nash non-cooperative equilibrium; (iii) utilitarian cooperative solution; and (iv) Lindahl cooperative solution. These were calibrated using Shared Socioeconomic Pathways (SSPs) to model regional population trajectories. The results highlight differences. By 2100, the no-policy case generates between 30% and 42% higher global emissions than Lindahl, depending on the SSP. The difference between the Nash and Lindahl solutions remains in light of the SSP1 trajectory, in which emissions are 28% higher in China, 6% higher in Brazil and 23% higher globally under non-cooperative behaviour. In addition, incorporating energy into cooperative solutions reduces long-term emissions levels and social carbon costs when compared to classic RICE. In the cooperative scenario, global emissions converge between SSPs, varying by less than 3% by 2100. This adjustment occurs via increased mitigation efforts and SCC, which grows by more than 200% between 2020 and 2100, varying between scenarios. Cumulative global welfare is higher under Lindahl, with long-term gains increasing. Model comparisons are useful for evaluating integrated policies, and this study provides valuable insights into international climate cooperation.

In liberalized power systems, generation investment is undertaken by decentralized, profit-maximizing agents, while transmission expansion remains centrally planned. Efficient system development therefore requires effective coordination between generation and transmission investments. In practice, however, transmission projects are subject to uncertainty and delays due to permitting processes, land acquisition constraints, and social opposition. Such frictions can distort generation investment decisions and reduce system-wide economic efficiency. This challenge is further intensified by the global energy transition, which requires large-scale transmission expansions to integrate renewable resources located in remote areas, such as offshore wind and desert-based solar. In this context, risk-averse generation investors may respond to transmission uncertainty by adopting hedging strategies that lead to socially suboptimal outcomes, including the selection of locations that are less exposed to transmission risk but yield lower system-wide welfare.
This paper proposes a mechanism based on congestion-rent allocation aimed at restoring long-term investment efficiency in electricity markets without Financial Transmission Rights. By reallocating congestion revenues to improve the private returns of socially desirable projects, the mechanism aligns private incentives with system-wide welfare objectives and assigns congestion-rent allocation a new role as a risk-hedging instrument. We develop a tri-level market equilibrium model in which generation investment decisions interact with network constraints at the operational level and with uncertain transmission investments. The framework allows us to evaluate alternative congestion-rent allocation schemes and identify the corresponding long-term equilibrium outcomes.
Numerical results show that congestion-rent allocation schemes can affect generation investment outcomes, both in installed capacity and location. By comparing long-term market equilibria with socially optimal planner benchmarks, we identify welfare-enhancing schemes from a cost-effective perspective. The findings underscore congestion-rent design and explicitly modeling transmission–generation interactions when assessing long-term system efficiency.

Motivation: Despite increasing electricity generation in the EAC region, fragmented governance quality and divergent national pricing frameworks continue to hinder cross-border power trade, price stability and harmonization. This study adopts a probabilistic approach (Quantile regression model) to examine the effect of governance quality on electricity price forecasting. Specifically, this study seeks to answer the following research question; what is the effect of governance quality on electricity price forecasting in the EAC region?
Methodology: This study uses a longitudinal panel data from the World Bank for five East African Community countries (Uganda, Kenya, Rwanda, Tanzania, and Burundi) covering 2010–2024, and applied a quantile regression model in STATA to examine the effect of governance quality indicators on electricity prices across different points of the price distribution (low, median, and high tariff levels).
Findings: The quantile regression results indicates that, in East African Community states, the effects of governance quality on electricity price forecasts differ across the price distribution: control of corruption and political stability exert stronger impacts at higher price levels, while government effectiveness and rule of law are more influential at lower and median prices, but with limited influence on higher price levels. 
Implication: Therefore, the results justifies the need for targeted governance reforms, such as regulatory governance reforms, in EAC states to strengthen institutional quality and align national pricing frameworks, This will stabilize and harmonize electricity prices across different market conditions, promoting cross-border trade, and advancing the objectives of Eastern Africa Power Pool (EAPP).

The European Union aims for substantial hydrogen imports by 2050. These imports can be transported via pipelines or shipping. Considering the proximity of potential suppliers, pipeline connections are generally more cost-effective than shipping. However, despite this economic advantage, pipeline infrastructure is associated with higher outage risks due to accidents or geopolitical factors. Incorporating these risks into hydrogen expansion planning necessitates a stochastic modelling approach.
We develop a two-stage stochastic model of European hydrogen imports for 2050. The model covers the European Union aggregated in five demand nodes, as well as close-by supply countries in Northern Africa, countries bordering the North Sea, and a global market with ammonia as liquid hydrogen carrier. We maximise the social welfare on the European import market including a linear demand-price reaction across three different scenarios. First, a risk-free 'perfect' world that is used as benchmark. Second, a risk-ignoring ('naïve') scenario, and third, an 'anticipating' scenario considering outage risks. We assume that pipeline connections are subject to independent, probabilistic outage risks and take into account all possible realisations of this risk.
We find, that the risk-ignoring ('naïve') approach loses up to 16 % of total welfare compared to a risk-free benchmark. In contrast the stochastic optimal ('anticipating') 'wins' 12 % compared to the 'naïve' approach with a remaining 4 % difference to the risk-free benchmark. This highlights the economic importance and feasibility of a more resilient import infrastructure. Moreover, we show that optimal infrastructure with risk anticipation features terminal capacities for shipping as a back-up to risky pipelines as well as an intra-European distribution network. This emphasises the importance of considering failure risks in infrastructure planning rather than purely minimising costs.

Achieving net-zero emissions requires not only reducing greenhouse gases domestically, but also accounting for the full social costs of energy transitions across global supply chains. This paper integrates life-cycle assessment (LCA) with national energy system scenarios to economically value health externalities associated with Switzerland's net-zero pathway.
Using region-specific characterization factors and a 56-region damage framework, we quantify disability-adjusted life years (DALYs) attributable to climate change and particulate matter (PM₂.₅) formation across the entire life cycle of energy technologies. These impacts are monetized using value-of-life-year (VLYL) estimates to derive external cost intensities (CHF/kWh) for major sectors and technologies.
Results show that the 2050 net-zero pathway reduces total health damages by approximately 54% relative to 2020. However, more than 80% of the remaining burden occurs outside Switzerland, primarily in European and Asian supply chains. Electrified transport exhibits comparatively low social cost intensities, while small-scale biomass combustion remains a high-cost option with predominantly domestic health impacts.
By embedding life-cycle externalities into energy system modeling, this study provides decision-makers with economically consistent metrics to align decarbonization targets with health co-benefits and cleaner upstream supply chains. The framework offers a transferable approach for integrating environmental externalities into energy policy evaluation.