Modeling the UK’s Carbon Budget: An Economic Reality Check

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Author: Denis Avetisyan

A new agent-based model assesses the macroeconomic and distributional consequences of the UK’s Seventh Carbon Budget, offering crucial insights for policymakers.

This study details a data-driven agent-based macroeconomic model to evaluate the economic and inequality impacts of achieving the UK’s Seventh Carbon Budget by 2026.

Conventional macroeconomic modeling often struggles to capture the heterogeneous impacts of large-scale policy interventions, particularly those related to climate change. This paper, ‘Agent-based macroeconomics for the UK’s Seventh Carbon Budget’, details the development of a data-driven agent-based model to assess the economic and distributional consequences of the UK’s seventh carbon budget (CB7), scheduled to inform policy decisions by June 2026. Initial results demonstrate a framework for integrating decarbonisation pathways as shocks to financial flows and technical coefficients within a dynamic macroeconomic system. Will this approach provide policymakers with more nuanced insights into the trade-offs inherent in achieving ambitious climate targets and fostering inclusive economic growth?

Navigating Complexity: Carbon Budgets and Economic Forecasting

The United Kingdom’s legally binding carbon budgets, established by the Climate Change Act of 2008, necessitate increasingly precise economic forecasting to ensure feasibility and minimize disruption. These budgets aren’t simply aspirational goals; they represent firm commitments requiring detailed pathways for emissions reduction across all sectors of the economy. Consequently, policymakers require sophisticated models capable of projecting the economic impacts of decarbonization policies – including costs, benefits, and distributional effects. Accurate forecasting is paramount not only for tracking progress towards these budgets – such as the challenging Carbon Budget 7 – but also for informing strategic investment decisions and maintaining public support for the transition to a low-carbon economy. Failure to adequately anticipate economic consequences could lead to ineffective policies, increased costs, or even jeopardize the UK’s climate commitments.

Conventional macroeconomic forecasting relies on established relationships between economic variables, yet often falls short when predicting the impacts of rapid technological shifts and evolving consumer behavior. These models typically assume gradual change and stable preferences, failing to adequately represent the accelerating pace of innovation in decarbonization technologies – such as renewable energy, carbon capture, and energy storage – and the unpredictable ways households adopt them. This limitation presents a significant challenge for policymakers aiming to meet ambitious carbon budgets, as traditional assessments may underestimate the costs or overestimate the effectiveness of climate policies. Accurately capturing these dynamic interactions is therefore essential for informed decision-making and effective long-term planning in a transitioning economy; without it, projections can be misleading and hinder progress towards a low-carbon future.

Achieving the UK’s Carbon Budget 7 (CB7) necessitates a detailed understanding of how decarbonization technologies interact with choices made by individual households. Simply developing new low-carbon options – such as heat pumps or electric vehicles – is insufficient; successful implementation hinges on widespread adoption. Research indicates that household uptake isn’t solely determined by cost or performance, but also by behavioral factors like awareness, social norms, and pre-existing infrastructure. Therefore, accurately modeling this dynamic interplay – encompassing technological advancements, evolving consumer preferences, and the speed of infrastructure changes – is paramount. Failing to account for these complex feedback loops can lead to overestimation of potential emissions reductions and misdirected policy interventions, jeopardizing the nation’s commitment to ambitious climate goals.

A Systemic Approach: The Agent-Based Model

The INET Oxford Macroeconomic Model, a data-driven agent-based model, is utilized to simulate economic conditions and evaluate the potential impacts of CB7 policies. This model departs from conventional macroeconomic frameworks by explicitly representing the economy as a complex system of interacting individual agents – households and firms – rather than aggregating behavior at a national level. Simulations are conducted using a computationally intensive approach to track the evolving behavior of these agents and aggregate outcomes to assess macroeconomic effects. The model’s architecture allows for the analysis of emergent phenomena and feedback loops not readily captured by equation-based models, providing a more nuanced understanding of policy interventions.

The INET Oxford Macroeconomic Model departs from conventional macroeconomic modeling by utilizing an agent-based framework where each household is modeled as an individual ‘agent’ with unique characteristics and decision-making processes. These agents are interconnected within a defined network structure, enabling the simulation of social interactions and the propagation of information. This network representation allows researchers to analyze how behavioral responses – such as changes in consumption or savings – are influenced by the actions and characteristics of other households within the economic system, capturing emergent phenomena not readily apparent in aggregate models. The model facilitates the study of phenomena like herd behavior, opinion formation, and the impact of localized shocks that spread through the network via social connections.

The INET Oxford Macroeconomic Model utilizes data from the Wealth and Assets Survey and the Family Resources Survey for calibration, ensuring the simulated population reflects the characteristics of the UK household sector. These datasets provide detailed information on household income, wealth distribution, consumption patterns, and debt levels. To further enhance representativeness and address potential biases in the raw survey data, the PolicyEngine is integrated; this allows for weighting and imputation techniques to correct for non-response and ensure the simulated population accurately mirrors the demographic and socioeconomic composition of the UK as defined by official statistics.

Technological Momentum: Cost Reduction and Adoption

The model accounts for cost reduction in renewable energy technologies – specifically solar and wind power – through the application of Wright’s Law. This principle posits an inverse relationship between the cost of a technology and the cumulative volume of production; as total production increases, the unit cost decreases due to factors such as manufacturing efficiencies, economies of scale, and learning by doing. The model implements this by linking cost reductions to projected increases in global production of solar and wind, allowing for dynamic cost adjustments based on simulated deployment scenarios. This learning mechanism is critical for forecasting long-term energy costs and evaluating the economic viability of renewable energy sources.

The model forecasts the deployment of renewable energy technologies using S-Curve Adoption Dynamics, a methodology that maps technology diffusion over time. This approach leverages historical deployment data alongside projected cost reductions derived from technological learning-specifically, decreasing costs associated with increased cumulative production. Current projections, based on this modeling, indicate global wind energy costs will reach 43 USD/MWh by the year 2050. This forecast is based on continued cost declines and increasing adoption rates, modeled through the S-Curve framework and validated with simulation-based inference techniques.

Simulation-Based Inference was employed to refine model parameters, validating the correlation between technology costs, adoption rates, and resultant emissions levels. This calibration process yielded projections indicating a potential lower bound of 35 USD/MWh for global wind energy costs by 2050. Global solar energy costs are projected to range between 3 and 15 USD/MWh by the same year, reflecting anticipated continued declines due to technological learning and increased production volume. These figures represent modeled estimates based on historical data and projected trends, and are subject to change based on evolving technological and economic factors.

The Network Effect: Social Dynamics and Adoption Pathways

The INET Oxford Macroeconomic Model incorporates a crucial element often overlooked in climate change mitigation strategies: how individuals learn from each other. Rather than assuming households make decisions in isolation, the model represents the population as a network of interconnected ‘Household Nodes’. These nodes aren’t simply economic actors; they actively share information and observe the choices of their peers regarding the adoption of sustainable technologies like heat pumps and solar panels. This ‘social learning’ mechanism is embedded within the model’s structure, allowing researchers to simulate how the diffusion of these technologies is influenced by factors such as the strength of social connections and the visibility of early adopters. Consequently, the model demonstrates that adoption isn’t solely driven by economic incentives or technological advancements, but is profoundly shaped by the dynamics of this interconnected network.

The diffusion of low-carbon technologies, such as heat pumps and solar panels, isn’t solely driven by economic incentives or practical considerations; instead, information sharing and the influence of peers play a crucial role. Research utilizing network models demonstrates that individuals are heavily influenced by the adoption decisions of those within their social circles. If neighbors or friends embrace a sustainable technology, it significantly increases the likelihood of others doing the same, creating a ripple effect. Conversely, a lack of visible adoption, or negative experiences shared within a network, can impede progress, even if the technology is demonstrably beneficial. This highlights the importance of understanding these ‘peer effects’ – how social interactions can either accelerate or hinder the uptake of solutions vital for achieving climate targets, and emphasizes the potential for policy interventions designed to leverage social networks to promote sustainable practices.

Simulations within the INET Oxford Macroeconomic Model demonstrate a surprising potency of social networks in driving the adoption of sustainable technologies. Even relatively small degrees of social influence – where households learn from and are affected by the choices of their peers – can dramatically enhance the effectiveness of policy interventions aimed at promoting low-carbon solutions like heat pumps and solar panels. This amplification effect means that achieving ambitious climate targets, such as the CB7 goals, may be significantly more attainable than previously thought, not necessarily through substantially larger financial incentives, but through strategies that leverage and encourage information sharing and positive peer effects within communities. The model suggests that fostering social learning represents a powerful, and potentially cost-effective, complement to traditional policy approaches.

The modeling undertaken in this study, meticulously detailing the economic ramifications of the UK’s Seventh Carbon Budget, echoes a holistic understanding of interconnected systems. Each agent within the model, representing a sector of the economy, responds to policy shifts and technological advancements, creating ripple effects throughout the entire structure. This approach aligns with Thoreau’s observation that “Rather than love, than money, than fame, give me truth.” The pursuit of an accurate, data-driven simulation – a ‘truthful’ representation of the complex interplay between carbon reduction policies and macroeconomic outcomes – is paramount. The model’s ability to analyze inequality alongside economic growth underscores the importance of understanding the whole, not merely optimizing for a single metric, as structural changes inevitably redistribute benefits and burdens.

Where Do We Go From Here?

This work, attempting to map the circulatory system of a national economy responding to imposed constraints, reveals as much about the limitations of the map itself as it does about the territory. The model, while data-driven, remains an abstraction – a necessary simplification, yet one that inevitably obscures the myriad feedback loops and emergent behaviors inherent in a complex system. To treat carbon reduction as a purely macroeconomic problem is akin to diagnosing a fever without considering the patient’s underlying health.

Future iterations must move beyond calibration to validation. Demonstrating predictive power-not merely replicating past trends-requires integrating higher-resolution data, acknowledging the heterogeneous nature of ‘agents,’ and explicitly modeling technological learning as a co-evolutionary process. The current focus on the seventh carbon budget is useful, but the true test lies in the model’s ability to anticipate unintended consequences – the blockages in the bloodstream that appear only under stress.

Ultimately, the challenge is not simply to predict the economic impact of climate policy, but to understand how policy shapes the very structure of the economic system. A truly robust model will not offer a single ‘optimal’ pathway, but a range of plausible futures, each reflecting a different set of structural choices. The model, then, becomes a laboratory for policy experimentation – a tool for navigating the inevitable uncertainties that lie ahead.

Original article: https://arxiv.org/pdf/2602.15607.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-19 06:15