Modeling Uncertainty in Large Natural Resource Allocation Problems

The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields

A quantitative framework to evaluate urban ecological resilience: broadening understanding through multi-attribute perspectives

Intensive human and economic activities in urban areas have had adverse effects on local resources and ecology, leading to a decline in ecological resilience. Enhancing ecological resilience is crucial for improving the urban ecosystem's ability to withstand and recover from external risks. However, quantitative research on urban ecological resilience remains somewhat ambiguous, with many studies lacking comprehensive assessment methods from multiple perspectives. In this study, we established a comprehensive framework to assess urban ecological resilience based on four regime attributes. The study's results indicated the following key findings: The average urban ecological resilience value exhibited a trend of initially declining and then recovering. Cities proposed different approaches when considering and managing social and ecological relationships during the development process. A significant correlation between urbanization levels and ecological resilience was observed, with urban ecological resilience increasing in areas with low urbanization levels and sharply decreasing in areas with high urbanization levels. The findings from this study provide a specific theoretical foundation for decision-makers involved in urban planning and development strategies

To Build or not to Build? Capital stocks and climate policy

We investigate (i) the impact of emission reduction policy on investment in polluting infrastructure, such as coal-fired power stations and (ii) optimal subsidies for “clean” alternatives with “learning” spillovers. We build a general theoretical model, and embed it in a fully calibrated integrated assessment model. Because emission reduction policy reduces investments in polluting assets, short-term emission reductions are enhanced—our “irreversibility effect”. Thus, “stranded assets” in this fuel-using sector have distinctive properties. We also provide a simple formula for how the optimal subsidy to deployment of a “clean” sector depends on its rate of “learning-by-doing” and on its socially-optimal growth. So, if the sector should grow faster for other reasons, its optimal subsidy is increased, showing that its optimal growth rate is faster still—our “acceleration effect”. Our calibrations show that, to limit global climate change to 2○C warming, investments in coal-fired power stations must end very soon. Considering second-best settings, we show that carbon taxes achieve stringent policy targets more efficiently, but subsidies to the “clean” sector deliver higher welfare, and are more efficient, when policy targets are more mild