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Electric power planners in developing countries have the opportunity to anticipate air conditioning-induced demand peaks, rather than merely react to them, as has happened in the United States. Demand-side management activities targeting future air conditioning installations therefore may be warranted, based on economic efficiency considerations, the U.S. experience, and worldwide trends in air conditioning usage. The feasibility of such anticipatory efforts is likely to hinge on local parameters, including domestic energy supplies, foreign exchange and capital constraints, rates of load growth, evidence of impacts on load profiles, and familiarity with demand-side planning concepts.

In the agricultural sector, climate change (CC) affects multiple weather variables at different stages of crop cycles. CC may influence the mean level or affect the distribution of events (e.g., rainfall, temperature). This work evaluates the economic impact of CC-related changes in multiple climatic components, and the resulting uncertainty. For this purpose, a three-stage discrete stochastic programming model is used to represents farm sector of an irrigated area of Italy and to examine the influence of CC on rainfall and on maximum temperature. These variables affect the availability of water for agriculture and the water requirements of irrigated crops. The states of nature, and their change, are defined more broadly than in previous analyses; this allows examining the changes of more climatic variables and crops cultivation. The effect of CC is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future. The results show that the agricultural sector would seek to lower costs by modifying patterns of land use, farming practices and increasing the use groundwater. The overall economic impact of these changes is small and due primarily to the reduced availability of water in the future. The temperature increase is, in fact, largely offset by the effects of the increase in CO2 levels, which boosts the yield of main crops of the irrigated zone. Therefore, availability and water management becomes a crucial factor to offset the increase of evapotranspiration and of water stress resulting from the increase of temperature. However, the costs of CC are very high for some types of farming, which suffer a large reduction in income.

This paper examines the history of the various actual and proposed interconnectors between New South Wales and Victoria into South Australia. It covers the period from the earliest proposal for a regulated interconnector to the recent Victoria Supreme Court review and the latest ministerial proposals. It finds, inter alia, that the Supreme Court decision is likely to have strengthened, in a beneficial way, the regulatory regime for dealing with merchant interconnectors and the obligations on incumbent transmission companies. It finds that none of the proposals for regulated interconnectors did or would have passed the regulatory tests as formulated in terms of aggregate benefits to all market participants. It finds that neither of the merchant interconnectors (into South Australia and Queensland) are likely to have been profitable. It sees a possible explanation for the construction of regulated interconnectors in terms of the benefits to customers, or in terms of bringing about a single competitive market. Above all, it illustrates the political context in which decisions on interconnectors have been made, and the need to take account of such motivations when comparing the likely effects of regulated interconnectors versus merchant interconnectors

This study explores the impact of oil depletion on the energetic efficiency of oil extraction and refining in California. These changes are measured using energy return ratios (such as the energy return on investment, or EROI). I construct a time-varying first-order process model of energy inputs and outputs of oil extraction. The model includes factors such as oil quality, reservoir depth, enhanced recovery techniques, and water cut. This model is populated with historical data for 306 California oil fields over a 50 year period. The model focuses on the effects of resource quality decline, while technical efficiencies are modeled simply. Results indicate that the energy intensity of oil extraction in California increased significantly from 1955 to 2005. This resulted in a decline in the life-cycle EROI from 6.5 to 3.5 (measured as megajoules (MJ) delivered to final consumers per MJ primary energy invested in energy extraction, transport, and refining). Most of this decline in energy returns is due to increasing need for steam-based thermal enhanced oil recovery, with secondary effects due to conventional resource depletion (e.g., increased water cut).

Abstract:While the need to update EPA benefit-cost analysis to reflect the most recent science is broadly acknowledged, little work has been done examining how well ex ante BCAs estimate the actual benefits and costs of regulations. This paper adds to the existing literature on ex post cost analyses by examining EPA’s analysis of the 1998 Locomotive Emission Standards. Due to data limitations and minimal ability to construct a reasonable counterfactual for each component of the cost analysis, the assessment relies mainly on industry expert opinion, augmented with ex post information from publicly available data sources when possible. The paper finds that the total cost of bringing line-haul locomotives into compliance with the 1998 Locomotive Emission Standards rule remains uncertain. Even though the initial per-unit locomotive compliance costs were higher than predicted by EPA, total costs also depend on the number of locomotives affected by the regulation. Over 2000–2009, the number of newly built line-haul locomotives was higher but the number of remanufactured line-haul locomotives was lower than EPA’s estimate.

This paper investigates the meaning of systemic risk in energy markets and proposes a methodology to measure it. Energy Systemic Risk is defined by the risk of an energy crisis raising the prices of all energy commodities with negative consequences for the real economy. Measures of the total cost (EnSysRISK) and the net impact (ΔMES) of an energy crisis on the rest of the economy are proposed. The measures are derived from the Marginal Expected Shortfall (MES) capturing the tail dependence between the asset and the energy market factor. The adapted MES accounts for causality and dynamic exposure to common latent factors. The methodology is applied to the European Energy Exchange and the DAX industrial index, where a minor decline in industrial productivity is observed from recent energy shocks.

AbstractSlowing climate change will almost certainly require a reduction in greenhouse gas emissions, but agreement on who should reduce emissions by how much is difficult, in part because of the self-serving bias—the tendency to believe that what is beneficial to oneself is also fair. Conducting surveys among college students in the United States and China, we show that each of these groups displays a nationalistic self-serving bias in judgments of a fair distribution of economic burdens resulting from mitigation. Yet, we also show, by disguising the problem and the identity of the parties, that it is possible to elicit perceptions of fairness that are not influenced by national interests. Our research reveals that the self-serving bias plays a major role in the difficulty of obtaining agreement on how to implement emissions reductions. That is, the disagreement over what constitutes fair climate policy does not appear to be due to cross-national differences in what constitutes a fair distribution of burdens. Interventions to mitigate the self-serving bias may facilitate agreement.

The Urban Heat Island (UHI) is the phenomenon of altered increased temperatures in urban areas compared to their rural surroundings. UHIs grow and intensify under extreme hot periods, such as during heat waves, which can affect human health and also increase the demand for energy for cooling. This study applies remote sensing and land use/land cover (LULC) data to assess the cooling effect of varying urban vegetation cover, especially during extreme warm periods, in the city of Munich, Germany. To compute the relationship between Land Surface Temperature (LST) and Land Use Land Cover (LULC), MODIS eight-day interval LST data for the months of June, July and August from 2002 to 2012 and the Corine Land Cover (CLC) database were used. Due to similarities in the behavior of surface temperature of different CLCs, some classes were reclassified and combined to form two major, rather simplified, homogenized classes: one of built-up area and one of urban vegetation. The homogenized map was merged with the MODIS eight-day interval LST data to compute the relationship between them. The results revealed that (i) the cooling effect accrued from urban vegetation tended to be non-linear; and (ii) a remarkable and stronger cooling effect in terms of LST was identified in regions where the proportion of vegetation cover was between seventy and almost eighty percent per square kilometer. The results also demonstrated that LST within urban vegetation was affected by the temperature of the surrounding built-up and that during the well-known European 2003 heat wave, suburb areas were cooler from the core of the urbanized region. This study concluded that the optimum green space for obtaining the lowest temperature is a non-linear trend. This could support urban planning strategies to facilitate appropriate applications to mitigate heat-stress in urban area.

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.