Interfuel Substitution and Energy Use in the UK Manufacturing Sector

This paper investigates interfuel substitution in the UK manufacturing sector. Econometric models of interfuel substitution are applied to energy inputs aggregated by their energy use, and separately for thermal heating processes, where interfuel substitution is technologically feasible. Compared to aggregate data, estimated own-price fuel demand elasticities for all fuels and cross-price elasticities for fossil fuels are considerably higher for thermal heating processes. Nonetheless,electricity is found to be a poor substitute for other fuels based on both aggregate data and separately for the heating process. This study also finds that an increase in real fuel prices resulted in higher substitution elasticities based on aggregate data, and lower substitution elasticities for the heating process. The results of counterfactual decomposition of change in the estimated elasticities indicate that technological change was the major determinant of the differences in observed elasticities before and after the energy price increase.United Kingdom

Operational and Investment Response to Energy Prices in the OECD Manufacturing Sector

This paper estimates the vintage capital model of energy demand and examines operational and investment responses to energy prices at disaggregate level using data from five OECD manufacturing industries. Applying the model to less aggregate level data helps avoiding the distortions from exogenous structural shifts and measurement errors. The results confirm the previous findings that including capital stock vintages significantly improves the econometric model's goodness of fit. Estimated own-price elasticities of energy demand vary between 0.26 and 1.00 and are economically sound. Estimated own-price investment elasticities of energy efficiency of capital stock vary between 0.03 and 0.9. The investment response to energy prices thus varies significantly across manufacturing industries, being significant in some of them and negligible in other. The results of policy simulations for the U.K. petrochemical industry (the most energy-intensive industry in the sample) indicate that total (operational and investment) own-price elasticity of energy demand is close to one

Estimating market power in homogeneous product markets using a composed error model

This study contributes to the literature on estimating market power in homogenous product markets. We estimate a composed error model, where the stochastic part of the firmﾒs pricing equation is formed by two random variable

Estimating market power in homogenous product markets using a composed error model: application to the California electricity market

This study contributes to the literature on estimating market power in homogenous product markets. We estimate a composed error model, where the stochastic part of the firm’s pricing equation is formed by two random variables: the traditional error term, capturing random shocks, and a random conduct term, which measures the degree of market power. Treating firms’ conduct as a random parameter helps solving the issue that the conduct parameter can vary between firms and within firms over time. The empirical results from the California wholesale electricity market suggest that realization of market power varies over both time and firms, and reject the assumption of a common conduct parameter for all firms. Notwithstanding these differences, the estimated firm-level values of the conduct parameter are closer to Cournot than to static collusion across all specifications. For some firms, the potential for realization of the market power unilaterally is associated with lower values of the conduct parameter

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

Contracting for the second best in dysfunctional electricity markets

Power pools constitute a set of sometimes complex institutional arrangements for efficiency-enhancing coordination among power systems. In many developing countries, where such institutional arrangements can't be established over the short term, there still can be scope for voluntary electricity-sharing agreements among power systems. Using a particular type of efficient risk-sharing model with no commitment we demonstrate that second-best coordination improvements can be achieved with low to moderate risks of participants leaving the agreement. In the absence of an impartial market operator who can observe production fluctuations in connected power systems, establishing quasi-markets for trading excess electricity helps to achieve some cooperation in mutually beneficial electricity sharing

The science of impact and the impact of agricultural science

Research impact and its measurement are of increasing importance. This is particularly significant for agricultural science, which is expected to produce solutions to future challenges that will arise from population growth, climate change and ecosystem degradation. Much econometric effort has been devoted to analysis of investment in agricultural research and its effects on farm productivity. This analysis, reviewed here, has produced a consensus suggesting that returns are high, although they are achieved only after long lags. However, policymakers perceive the occurrence of impacts as too few, and poorly targeted with respect to their needs. An attribution gap between the outcomes of agricultural research and how they reach farmers has motivated evaluation of the process of transmission and translation of agricultural research outputs into ultimate impacts. This gap can be narrowed by Participatory Impact Pathway Analysis, implemented mostly so far in low income countries. However, it is a costly and cognitively complex approach. Content analysis of the UK’s 2014 REF Impact Case Studies uncovers the mind set of researchers and their managers regarding the description of impact and how it is supposed to occur. This reveals a nascent conservatism that focuses on research that can be shown to have impact, rather than research impact itself. From the overall discussion it can be concluded that the impact evaluation of agricultural science raises more profound issues than either efficiency or transparency. Confirmation bias threatens impact evaluation, principally by distracting from other important stories about how and why the ultimate effects occur, but also by transforming the nature of the process itself. Methodological pluralism, with greater integration and triangulation between different evaluation approaches, is a promising means of resolving these problems