• Energy Research

Abstract The aim of this paper is to re-examine the relationship between coal consumption and real GDP of China with the use of panel data. This paper applies modern panel data techniques to help shed light on the importance of the heterogeneity among different regions within China. Empirical analyses are conducted for the full panel as well as three subgroups of the panel. The empirical results show that coal consumption and GDP are both I(1) and cointegrated in all regional groupings. Heterogeneity is found in the GDP equation of the full panel. The regional causality tests reveal that the coal consumption–GDP relationship is bidirectional in the Coastal and Central regions whereas causality is unidirectional from GDP to coal consumption in the Western region. Thus, energy conservation measures will not adversely affect the economic growth of the Western region but such measures will likely encumber the economy of the Coastal and Central regions, where most of the coal intensive industries are concentrated.

Texas’s windfarm construction continues unabated, despite wind energy’s cannibalization effect on wind generation’s investment incentive and the rising popularity of short-term wind power purchase agreements (PPAs). Using ERCOT’s monthly data for Jan-2011 to Dec-2021, we develop spot energy price forecasts by time of day (TOD) and their standard deviations to derive the efficient frontiers (EFs) for spot and forward energy sales of a risk-averse windfarm developer under a short-term wind PPA of not more than ten years. These EFs reveal a windfarm’s operating revenue forecast tends to increase with the PPA’s forward energy prices. Further, the windfarm’s revenue risk and forecast move in tandem, akin to the risk-return relationship rooted in Markowitz’s portfolio theory. Hence, the developer tends to sell one hundred percent (<100%) of the windfarm’s energy output at forward energy prices that are above (below) spot energy price forecasts by TOD for recovering wind generation’s levelized cost of energy. JEL Classification: Q41, Q42, Q47, Q48

Abstract Using a panel data analysis of a newly developed sample of monthly data by state for January 2005 to December 2019, we estimate a series of error correction models for US residential electricity demand postulated to move with electricity price, natural gas price, income, and weather. Our key findings are as follows. First, the short-run own-price elasticity estimate is not statistically different from zero (p-value > 0.8). Second, the long-run own- and cross-price elasticity estimates are −0.054 (p-value = 0.000) and 0.019 (p-value = 0.000) under the double-log specification, smaller in size than the long-run own- and cross-price elasticity estimates of −0.120 (p-value = 0.000) and 0.069 (p-value = 0.000) under the linear demand specification. Third, price elasticity estimates have been shrinking in size over time. Fourth, erroneously ignoring the panel data's cross-sectional dependence tends to more than double the long-run price elasticity estimates. Fifth, mismatching the timing of price information's availability and consumption decision leads to anomalous price elasticity estimates. Finally, our new empirics' key takeaway of low price-responsiveness supports continuation of energy efficiency standards and demand-side management programs.

A large sample of daily electricity consumption and pricing data are available from a pilot study conducted by BC Hydro in British Columbia (Canada) of its residential customers under optional time-varying pricing and remotely-activated load-control devices for the four winter months of November 2007– February 2008. We use those data to estimate the elasticity of substitution r, defined as the negative of the percentage change in the peak-to-off-peak kW h ratio due to a 1% change in the peak-to-off-peak price ratio. Our estimates ofr characterize residential price responsiveness with and without load control during cold-weather months. While the estimates of r sans load control are highly statistically significant (a = 0.01), they are less than 0.07. With load control in place, however, theser estimates more than triple. Finally, we show that time-varying pricing sans load control causes a peak kW h reduction of 2.6% at the 2:1 peak-to-off-peak price ratio to 9.2% at the 12:1 peak-to-off-peak price ratio. Load control raises these reduction estimates to 9.2% and 30.7%.

We use a profit-maximizing model of forward pricing, which revealed that an electricity retailer’s fixed price offer (FPO) contains a large forward premium that encourages vertical integration of a generation company and a retailer. However, vertical integration does not always reduce FPOs, particularly when residential customers are segmented by consumption size and price sensitivity. Hence, a proposed merger of a big generation company and a big retailer requires regulatory scrutiny.

Abstract China’s ambitious decarbonization strategy disaggregates the national energy conservation target by province. Using panel data of 30 provinces for 1995–2017, we revisit China’s energy-growth nexus that considers the likely cross-section dependence among the provinces within each of China’s three regions. Our key finding is a bidirectional causal relationship of energy (natural log of per capita energy consumption) and income (natural log of per capita real GDP) for the Eastern and Central regions and a unidirectional causal relationship from income to energy for the Western region. The Eastern and Central regions’ bidirectional relationship suggests caution in China’s energy conservation policy which may decelerate these regions’ economic growth. The Western region’s unidirectional relationship suggests promoting energy conservation without adversely affecting this region’s economic growth. Hence, the East and Central regions’ conservation effort should be accompanied by cost-effective development of emissions-free renewable resources like hydro, solar and wind for displacing China’s fossil-fuel consumption.

China is a global leader in methanol production volume, while coal is a major feedstock. The country also has the world’s largest commercial coal-to-methanol operations. Coal-based methanol is used widely within China and is a competitive substitute for gasoline. Owing to this, it is plausible that the price of coal may be linked to international crude oil prices, with methanol prices serving as the connecting channel. We add supporting evidence to a recently emerging area of literature and observe statistically significant relationships among the three prices, and, therefore, the influence from international crude oil and methanol prices on the coal price determination in China. This paper investigates the relationships among these prices for the period from January 2010 to December 2019 through spectral Granger causality analysis, alongside more traditional cointegration tests to develop a comprehensive picture of causal association between the price series in both the frequency and time domain. Cointegration is found in our tri-variate system while the frequency domain Granger causality tests reveal the long-run causality in all directions except from crude oil to methanol, thus, emphasizing the structure of coal price dependence. According to the generalized impulse response functions, the coal price reacts positively to shocks in crude oil prices.