Emptying the future: On the environmental politics of anticipation

Anticipation may be seen as structured by images and representations, an approach that has informed recent work in science and technology studies on the sociology of expectations. But anticipation, as a capacity or characteristic, is not solely manifested in the form of representations, even where such representations of the ‘not yet’ are performative in nature. It also comprises material capacities, technological, biophysical and affective in nature. The politics of anticipation is shaped by how these symbolic and material capacities, and the forms of agency they make possible, are distributed. As anticipation is an environmentally distributed capacity, it is suggested that the politics of anticipation is also an environmental politics. A conceptual framework for analysing anticipation as comprised of environmental capabilities is introduced, and fleshed out using a case study of energy infrastructure planning from the UK. Key elements of this framework include the concepts of anticipatory assemblages and future horizons or ‘styles’ of anticipation. Working through the case study as an empirical example of a conflict concerning the politics of anticipation and of ‘environments’, it is demonstrated how the relationships between styles of anticipation are materially constitutive of such conflicts.     

A multivariate time series analysis of energy consumption,real output and pollutant emissions in a developing economy: New evidence from Nepal

Mountain economies will have to play a central role in attaining the global pursuit of green economic growth as crucial bearers of ecosystems goods and services. However, these economies are not adequately represented in the development policy debates in spite of their fundamental importance towards global sustainable development. This study examines the inter relationships between energy consumption, output and carbon emissions in a developing mountainous economy using an augmented Vector Autoregression model. Time-series data over the period 1975–2013 is studied applying a multivariate framework using population and gross fixed capital formation as additional variables for Nepal. Testing for Granger causality between integrated variables based on asymptotic theory reveals a long-run unidirectional Granger causality running from GDP to energy consumption, and a unidirectional Granger causality running from carbon emissions to GDP. We suggest that the government of Nepal can address energy poverty by accelerating the adoption energy conservation policies such as rationing energy consumption and energy efficiency improvements to narrow the energy supply-demand gap. The opportunity to promote the uptake of decentralised off-grid renewable technologies in remote areas and the large scale development of hydropower at the national level also needs to be prioritized. Our results remain robust across different estimators and contributes to an emerging literature on the nexus relationships between energy consumption, income and carbon emissions in mountainous developing economies.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

Input use under cost-of-production crop insurance: Theory and evidence

There have been a number of previous studies that examined the effects of yield- or revenue-based crop insurance products on input use of farmers. However, no study has specifically investigated the input use impacts of a cost-of-production (COP) crop insurance policy, even though this type of crop insurance is the predominant one used in several other countries outside of the United States (such as the Philippines and China). This article aims to theoretically and empirically examine the effect of a COP crop insurance product on farmers’ chemical input use. Our theoretical model suggests that the effect of COP insurance on input use can either be positive or negative, with the resulting impact depending on the strengths of (a) the traditional moral hazard effect of insurance (i.e., an input use decreasing effect); versus (b) the marginal incentives to apply more inputs due to input levels being the main determinant for expected indemnity amounts in this type of insurance (i.e., an input use increasing effect). A survey data set from corn farmers in the Philippines is then used to empirically illustrate how a particular COP insurance product influences input use in a real-life context. In this case, we find that COP insurance increases the use of chemical inputs (e.g., fertilizers and total chemical expenditure), implying that the positive marginal incentive to apply more inputs dominates the negative moral hazard effect.     

Energy efficiency promotion backfires under cap-and-trade

Major climate-cum-energy policies and respective impact projections rest on the widespread belief that increased energy efficiency can be equated with savings in energy use and emissions. This belief is flawed. Due to the rebound effect emissions savings from energy efficiency improvements will be generally less than what is technically feasible, or even be reversed. By means of an analytical general equilibrium model we demonstrate the latter to be true in a case that is both stark and relevant: if electricity generation is subject to a cap-and-trade scheme with partial coverage, increased efficiency of electric devices leads unambiguously to increased carbon emissions. The result implies that a proper distinction between the energy rebound and the carbon rebound is warranted, and that public policy must carefully consider the interactions between energy efficiency promotion and carbon pricing.     

Asset pricing and energy consumption risk

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk-free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross-sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three-factor model in the cross-sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.     

Decision-making in incomplete markets with ambiguity—a case study of a gas field acquisition

We apply utility indifference pricing to solve a contingent claim problem, valuing a connected pair of gas fields where the underlying process is not standard Geometric Brownian Motion and the assumption of complete markets is not fulfilled. First, empirical data are often characterized by time-varying volatility and fat tails; therefore, we use Gaussian generalized autoregressive score (GAS) and GARCH models, extending them to Student’s t-GARCH and t-GAS. Second, an important risk (reservoir size) is not hedgeable. As a result, markets are incomplete which makes preference free pricing impossible and thus standard option pricing methodology inapplicable. Therefore, we parametrize the investor’s risk preference and use utility indifference pricing techniques. We use Least Squares Monte Carlo simulations as a dimension reduction technique in solving the resulting stochastic dynamic programming problems. Moreover, an investor often only has an approximate idea of the true probabilistic model underlying variables, making model ambiguity a relevant problem. We show empirically how model ambiguity affects project values, and importantly, how option values change as model ambiguity gets resolved in later phases of the projects. We show that traditional valuation approaches will consistently underestimate the value of project flexibility and in general lead to overly conservative investment decisions in the presence of time-dependent stochastic structures.     

Can investors of Chinese energy stocks benefit from diversification into commodity futures?

We attempt to evaluate the diversification potential of commodity futures for energy stocks in China. With a variety of copula functions and three risk-based dynamic measures, our results show that even though commodity futures are not helpful in improving the risk-adjusted returns of energy stocks, they can significantly reduce the volatilities and expected-shortfalls of the diversified portfolios. Such diversification benefits are much larger during large market downturns than during normal times. In particular, gold (copper) futures are the most (least) attractive in diversifying risks of energy stocks in most cases. The results also highlight that the non-linear dependence cannot be ignored when estimating the diversification benefits, and more various risk hedging strategies are expected for investors holding energy stocks, especially coal company stocks.