•Empirical validation is an open issue for simulation models.•An novel information theoretic criterion, called GSL-div, is introduced.•It measures the distance between the dynamics of a simulation ...and the empirical data.•It is tested in a series of model contests and compared to standard fit indicators.•It is found to correctly rank the models and perform better than the alternatives.
Simulated models suffer intrinsically from validation and comparison problems. The choice of a suitable indicator quantifying the distance between the model and the data is pivotal to model selection. An information theoretic criterion, called GSL-div, is introduced to measure how closely models’ synthetic output replicates the properties of observable time series without the need to resort to the likelihood function or to impose stationarity requirements. The indicator is sufficiently general to be applied to any model able to simulate or predict time series data, from simple univariate models to more complex objects including Agent-Based Models. When a set of models is given, a simple function of the L-divergence is used to select the candidate producing distributions of patterns that are closest to those observed in the data. The proposed approach is illustrated through three examples of increasing complexity where the GSL-div is used to discriminate among a variety of competing models. Results are compared to those obtained employing alternative measures of model’s fit. The GSL-div is found to perform, in the vast majority of cases, better than the alternatives.
Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming ...increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count - all proxies of economic activity - before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.
Abstract Induced innovation is a multi-faceted process characterized by interaction between demand-pull forces, path-dependent self-reinforcing change, and the cost reduction of technology that ...occurs with cumulative deployment. By endogenously including induced innovation in energy models, policy analysts and modellers could enable a mission-oriented approach to policymaking that envisions the opportunities of accelerating the low-carbon energy transition while avoiding the risks of inaction. While the integrated assessment models used in the intergovernmental panel on climate change (IPCC-IAMs) account for induced innovation, their assumptions of general equilibrium and optimality may reveal weaknesses that produce unsatisfactory results for policymakers. In this paper, we develop a menu of options for modelling induced innovation in the energy transition with non-equilibrium, non-optimal models by a three step methodology: a modelling survey questionnaire, a review of the literature, and an analysis of case studies from modelling applications within the economics of energy innovation and system transition (EEIST) programme. The survey questionnaire allows us to compare 24 models from EEIST partner institutions developed to inform energy and decarbonisation policy decisions. We find that only six models, future technological transformations, green investment barriers mode, stochastic e xperience c urves, economy-energy-environment macro-econometric, M3E3 and Dystopian Schumpeter meeting Keynes, represent endogenous innovation—in the form of learning curves, R&D, and spillover effects. The review of the literature and analysis of case studies allow us to form a typology of different models of induced innovation alongside the IPCC-IAMs and develop a decision tree to guide policy analysts and modellers in the choice of the most appropriate models to answer specific policy questions. The paper provides evidence for integrating narrow and systemic approaches to modelling-induced innovation in the context of low-carbon energy transition, and promotes cooperation instead of competition between different but complementary approaches. These findings are consistent with the implementation of risk-opportunity analysis as a policy appraisal method to evaluate low-carbon transition pathways.
Efficiently calibrating agent-based models (ABMs) to real data is an open challenge. This paper explicitly tackles parameter space exploration and calibration of ABMs by combining machine-learning ...and intelligent iterative sampling. The proposed approach “learns” a fast surrogate meta-model using a limited number of ABM evaluations and approximates the nonlinear relationship between ABM inputs (initial conditions and parameters) and outputs. Performance is evaluated on the Brock and Hommes (1998) asset pricing model and the “Islands” endogenous growth model Fagiolo and Dosi (2003). Results demonstrate that machine learning surrogates obtained using the proposed iterative learning procedure provide a quite accurate proxy of the true model and dramatically reduce the computation time necessary for large scale parameter space exploration and calibration.
A major concern about the use of simulation models regards their relationship with the empirical data. The identification of a suitable indicator quantifying the distance between the model and the ...data would help and guide model selection and output validation. This paper proposes the use of a new criterion, called
GSL-div
and developed in Lamperti (Econ Stat,
2017
.
https://doi.org/10.1016/j.ecosta.2017.01.006
), to assess the degree of similarity between the dynamics observed in the data and those generated by the numerical simulation of models. As an illustrative application, this approach is used to distinguish between different versions of the well known asset pricing model with heterogeneous beliefs proposed in Brock and Hommes (J Econ Dyn Control 22(8–9):1235–1274,
1998
.
https://doi.org/10.1016/S0165-1889(98)00011-6
). Once the discrimination ability of the
GSL-div
is proved, model’s dynamics are directly compared with actual data coming from two major stock market indexes (EuroSTOXX 50 for Europe and CSI 300 for China). Results show that the model, once calibrated, is fairly able to track the evolution of both the two indexes, even though a better fit is reported for the Chinese stock market. However, I also find that many different combinations of traders’ behavioural rules are compatible with the same observed dynamics. Within this heterogeneity, an emerging common trait is found: to be empirically valid, the model has to account for a strong trend following component, which might either come from a unique trend type that heavily extrapolates information from past observations or the combinations of different types with milder, or even opposite, attitudes towards the trend.
Climate change has increased the frequency and intensity of natural disasters. Does this translate into increased economic damages? To date, empirical assessments of damage trends have been ...inconclusive. Our study demonstrates a temporal increase in extreme damages, after controlling for a number of factors. We analyze event-level data using quantile regressions to capture patterns in the damage distribution (not just its mean) and find strong evidence of progressive rightward skewing and tail-fattening over time. While the effect of time on averages is hard to detect, effects on extreme damages are large, statistically significant, and growing with increasing percentiles. Our results are consistent with an upwardly curved, convex damage function, which is commonly assumed in climate-economics models. They are also robust to different specifications of control variables and time range considered and indicate that the risk of extreme damages has increased more in temperate areas than in tropical ones. We use simulations to show that underreporting bias in the data does not weaken our inferences; in fact, it may make them overly conservative.
•Climate risks affect both the real economy and the financial system.•We employ an agent-based macro-financial model to assess green financial policies.•Green capital requirements spur productivity ...without hampering financial stability.•Carbon risk adjustment and green public guarantees mitigate emissions but increase instability.•A policy mix comprising all three policies is a sufficient condition for a virtuous cycle.
Which policies can increase the resilience of the financial system to climate risks? Recent evidence on the significant impacts of climate change and natural disasters on firms, banks and other financial institutions call for a prompt policy response. In this paper, we employ a macro-financial agent-based model to study the interaction between climate change, credit and economic dynamics and test a mix of policy interventions. We first show that financial constraints exacerbate the impact of climate shocks on the economy while, at the same time, climate damages to firms make the banking sector more prone to crises. We find that credit provision can both increase firms’ productivity and their financial fragility, with such a trade-off being exacerbated by the effects of climate change. We then test a set of “green” finance policies addressing these risks, while fostering climate change mitigation: i) green Basel-type capital requirements, ii) green public guarantees to credit, and iii) carbon-risk adjustment in credit ratings. All the policies reduce carbon emissions and the resulting climate impacts, though moderately. However, their effects on financial and real dynamics is not straightforwardly positive. Some combinations of policies fuel credit booms, exacerbating financial instability and increasing public debt. We show that the combination of the three policies leads to a virtuous cycle of (mild) emission reductions, stable financial sector and high economic growth. Additional tools would be needed to fully adapt to climate change. Hence, our results point to the need to complement financial policies cooling down climate-related risks with mitigation policies curbing emissions from real economic activities.
Climate anomalies, such as floods and droughts, as well as gradual temperature changes have been shown to adversely affect economies and societies. Although studies find that climate change might ...increase global inequality by widening disparities across countries, its effects on within-country income distribution have been little investigated, as has the role of rainfall anomalies. Here, we show that extreme levels of precipitation exacerbate within-country income inequality. The strength and direction of the effect depends on the agricultural intensity of an economy. In high-agricultural-intensity countries, climate anomalies that negatively impact the agricultural sector lower incomes at the bottom end of the distribution and generate greater income inequality. Our results indicate that a 1.5-SD increase in precipitation from average values has a 35-times-stronger impact on the bottom income shares for countries with high employment in agriculture compared to countries with low employment in the agricultural sector. Projections with modeled future precipitation and temperature reveal highly heterogeneous patterns on a global scale, with income inequality worsening in high-agricultural-intensity economies, particularly in Africa. Our findings suggest that rainfall anomalies and the degree of dependence on agriculture are crucial factors in assessing the negative impacts of climate change on the bottom of the income distribution.
We develop a dynamic model where heterogeneous firms take investment decisions depending on their beliefs on future carbon prices. A policy-maker announces a forward-looking carbon price schedule but ...can decide to default on its plans if perceived transition risks are high. We show that weak policy commitment, especially when combined with ambitious mitigation announcements, can trap the economy into a vicious circle of credibility loss, carbon-intensive investments and increasing risk perceptions, ultimately leading to a failure of the transition. The presence of behavioural frictions and heterogeneity - both in capital investment choices and in the assessment of the policy-maker's credibility - has strong non-linear effects on the transition dynamics and the emergence of ‘high-carbon traps’. We identify analytical conditions leading to a successful transition and provide a numerical application for the EU economy.
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