A Model of Technological Growth under Emission Constraints

We suggest and analyze a model of global technological growth under a prescribed constraint on the annual emission of greenhouse gases (GHG). The model assumes that industrial GHG emission is positively related to the world's production output driven by the development of the "production"technology stock. "Cleaning" technology is developed in parallel to keep the annual GHG emission within a "safety" zone. The ratio between annual investment in "cleaning" technology and annual investment in "production" technology acts as a time-variable control parameter in the model. Under a set of natural assumptions we find an optimal control which maximizes an integral utility characterizing the rate of economic growth over a given time period. In substantial terms, the optimal control strategy suggests that "production" technology is developed at a maximum rate until a critical point is reached at which the annual emission hits the prescribed upper bound. In the subsequent period investment in "production" and "cleaning" technology is planned so that the annual emission "tracks" the prescribed upper bound. One should note that the proposed control strategy optimal with respect to the chosen utility, is the most risky one since it assumes a minimum distance to the boundary of the "safety" zone

Optimal Pollution and Optimal Population

There is emerging evidence that environmental degradation increases human mortality. This paper provides a long-run consumer maximization model where population growth is endogenous to emissions that are generated in production. There is a trade-off between consumption and population growth; large consumption calls for large production, thus leading to high environmental mortality and low population growth. It may be optimal to end up with negative population growth implying that demographic sustainability fails as consumption increases excessively. We provide a theoretical model and suggest its calibrated version using European air pollution data. Our exercise illustrates the functioning of the theoretical model and discusses related methodological problems. Journal of Economic Literature: Q01, Q53, Q54, J1

Reconciling Information from Alternative Climate-economic Models: A Posterior Integration Approach

Studies of complex systems are non-separable from the analysis of partial and imprecise information received from alternative sources. Due to the high complexity of the underlying processes, researches tend to create an ensemble of multiple models, which describe the studied phenomenon using different modeling approaches and primary assumptions. A system analysist deals then with a set of ensemble outcomes (usually represented by a family of probability distributions), which needs to be integrated into one estimate in order to install the ensemble into the modeling chain or provide support for the informed decision making. This research is focused on the application of the posterior integration method (which was originally developed in IIASA [1] to reconcile stochastic estimates from independent sources) to an ensemble of climate-economic models. Our case-study uses two versions of the stylized model SDEM (Structural Dynamic Economic Model) [2], which generate different outputs (including emissions, CO2 concentration, temperature, size of economy) under two scenarios: the business-as-usual scenario and mitigation scenario (under carbon tax). We compare original results with results of posterior integration and results of the traditional approach of averaging model outcomes. [1] Kryazhimskiy, A. (2013) Posterior integration of independent stochastic estimates, IIASA Interim Report IR-13-006. [2] Kovalevsky, D.V., Hasselmann, K. (2014): Assessing the transition to a low-carbon economy using actor-based system-dynamic models. Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs), 15-19 June 2014, San Diego, California, Vol. 4, 1865-1872, URL: http://www.iemss.org/sites/iemss2014/papers/Volume_4_iEMSs2014_pp_1817-2386.pdf

Reconciling Information From Climate-Economic Model Ensembles

To date, no model building process can ensure full representation of the complex climate-economic processes – instead, multiple highly detailed models are put forward by individual research groups to capture some selected aspects. On the other hand, a number of the simplified integrated assessment models (IAMs) have been developed attempting to consider the full causal loop between accumulated emissions, economy and climate, and study associated uncertainty. Here we present a simplified system dynamics IAM based on the model from Kovalevsky and Hasselmann (2014) with stochastic climate sensitivity and a nonlinear climate damage function. We explore the structural sensitivity of the long-term projections (focusing on global temperature, global economy output, GHG emissions and atmospheric concentrations) to a probabilistic distribution describing the climate sensitivity. We investigate the model robustness under different assumptions on climate sensitivity distribution. For this purpose, we use the approach suggested by Kryazhimskiy (2016), which attempts to ‘integrate’ several independent distributions representing the same variable into one posterior distribution using a Bayesian approach based on the posterior event being the one when stochastic variables in all models have the same realization. The results show that model ‘integration’ leads to a higher mean global output, emissions and concentrations and lower mean global temperature than both prior means, coming along with lower uncertainty in the integrated scenario. The authors would like to acknowledge DG research for funding through the FP7-funded COMPLEX project #308601, www.complex.ac.uk. 1. D.V. Kovalevsky, K. Hasselmann (2014): Assessing the transition to a low-carbon economy using actor-based system-dynamic models, Proceedings of the 7th International Congress on Environmental Modelling and Software (iEMSs), 15-19 June 2014, San Diego, California, 4, 1865-1872 2. A.V. Kryazhimskiy (2016): Posteriori integration of probabilities. Elementary theory, Theory Probab. Appl., 60:1, 62–8

Allocating Scarce Resources: Modeling to Support Food-Energy-Water Sustainability

Global freshwater supplies are increasingly under stress due to the combined effects of population growth, increasing industrialization, and climate change. As a result, water resources must be managed and allocated in ways that are economically efficient and that account for interdependencies between food production, energy generation, and water provision, often referred to as the food-energy-water nexus. Mathematical modeling provides the means to anticipate what might happen in the future based on certain conditions, allowing decision-makers to optimize water use across sectors while taking into account uncertainties over future water availability

Understanding the Drivers of Urban Expansion: Case Study of Seville Province

Urban development has accelerated across the globe in recent decades. Much of this development has not been concentrated in cities, but has occurred as dispersed, low-density development outside of major centers but within their area of economic influence, along transport networks, in coastal areas, or close to areas of high natural value. This research focuses on the case study of the province of Seville, Spain, which has experienced notable urban expansion in recent years. We present a systems approach to model dependence between economic development and distribution of urban and non-urban land in this region from data collection to model validation. An extensive search of available indicators of socioeconomic development in this region has been carried out. We apply this data to create a generic statistical model of urban expansion, which links land-use patterns with population density and other indicators of economic growth. The model is tested across the whole Seville region and in its sub-regions to derive drivers of urban expansion in this territory