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Forecasting China’s Carbon Intensity -- Is China on Track to Comply with Its Copenhagen Commitment?

Yuan Yang, Junjie Zhang, and Can Wang

Year: 2018
Volume: Volume 39
Number: Number 2
DOI: 10.5547/01956574.39.2.yyan
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Abstract:
In the 2009 Copenhagen Accord, China agreed to slash its carbon intensity (carbon dioxide emissions/GDP) by 40% to 45% from the 2005 level by 2020. We assess whether China can achieve the target under the business-as-usual scenario by forecasting its emissions from energy consumption. Our preferred model shows that China's carbon intensity is projected to decline by only 33%. The results imply that China needs additional mitigation effort to comply with the Copenhagen commitment. The emission growth is more than triple the emission reductions that the European Union and the United States have committed to in the same period.



A Spatial Stochastic Frontier Model with Omitted Variables: Electricity Distribution in Norway

Luis Orea, Inmaculada C. Álvarez, and Tooraj Jamasb

Year: 2018
Volume: Volume 39
Number: Number 3
DOI: 10.5547/01956574.39.3.lore
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Abstract:
An important methodological issue in efficiency analysis for incentive regulation of utilities is how to account for the effect of unobserved cost drivers such as environmental factors. We combine a spatial econometric approach with stochastic frontier analysis to control for unobserved environmental conditions when measuring efficiency of electricity distribution utilities. Our empirical strategy relies on the geographic location of firms as a source of information that has previously not been explored in the literature. The underlying idea is to utilise data from neighbouring firms that can be spatially correlated as proxies for unobserved cost drivers. We illustrate this approach using a dataset of Norwegian distribution utilities for the 2004-2011 period. We show that the lack of information on weather and geographic conditions can be compensated with data from surrounding firms. The methodology can be used in efficiency analysis and regulation of other utilities sectors where unobservable cost drivers are important, e.g. gas, water, agriculture, fishing.



Spatial Effects of Wind Generation and Its Implication for Wind Farm Investment Decisions in New Zealand

Le Wen, Basil Sharp, and Erwann Sbai

Year: 2020
Volume: Volume 41
Number: Number 2
DOI: 10.5547/01956574.41.2.lwen
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Abstract:
Spill-over effects on electricity nodal prices associated with increased wind generation have not been examined in the literature. To examine these effects, we use spatial econometric models to estimate the direct and indirect effects of wind generation on nodal wholesale electricity prices. Spatial econometric models allow us to provide quantitative estimates of spill-over magnitudes and statistical tests for significance. Results show negative and significant effects are associated with increases in wind penetration, and the effect is stronger during peak hours and weaker during off-peak hours. Simulation results demonstrate net savings of NZ$8 million per MW of additional wind capacity installed at the CNI2 wind site. The findings provide valuable information on the evaluation of wind farm development in terms of site location, wholesale prices, and financial feasibility. Our approach also contributes to forecasting location specific wholesale electricity prices, and provides a better understanding of the implications of locating wind sites.



Locational (In)Efficiency of Renewable Energy Feed-In Into the Electricity Grid: A Spatial Regression Analysis

Tim Höfer and Reinhard Madlener

Year: 2021
Volume: Volume 42
Number: Number 1
DOI: 10.5547/01956574.42.1.thof
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Abstract:
This paper presents an econometric analysis of curtailment costs of renewable energy sources (RES) in Germany. The study aims at explaining and quantifying the regional variability of RES curtailment, which is a measure to relieve grid overstress by temporarily disconnecting RES from the electricity grid. We apply a Heckit sample selection model, which corrects bias from non-randomly selected samples. The selection equation estimates the probability of occurrence of RES curtailment in a region. The outcome equation corrects for cross-sectional dependence and quantifies the effect of RES on curtailment costs. The results show that wind energy systems connected to the distribution grid increase RES curtailment costs by 0.7% per MW (or 0.2% per GWh) in subregions that have experienced RES curtailment over the period 2015-2017. The implication of this finding is that policymakers should set price signals for renewables that consider the regional grid overstress, in order to mitigate the cost burden on consumers due to excess generation from RES.





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