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The impacts of weather variation on energy demand and carbon emissions
Abstract
This paper examines the impacts of climate fluctuations on carbon emissions using monthly models of US energy demand. The econometric analysis estimates price, income, and weather elasticities of short-run energy demand. Our model simulations suggest that warmer climate conditions in the US since 1982 slightly reduced carbon emissions in the US. Lower energy use associated with reduced heating requirements offsets higher fuel consumption to meet increased air-conditioning needs. The analysis also suggests that climate change policies should allow some variance in carbon emissions due to short-term weather variations.
... Some scholars have directly studied the relationship between weather factors (such as temperature, precipitation, and relative humidity) and the carbon market, but these studies have not reached a consistent conclusion on whether weather changes significantly affect carbon prices. For example, Considine [16] and Zhao et al. [17] found that weather change is an important factor affecting carbon price in determining the influencing factors of the carbon trading market. However, the research results of Koch et al. [18] and Ji et al. [19] show that climate change does not significantly impact carbon prices. ...
... Firstly, weather changes can influence individuals' heating and cooling demands, affecting carbon pricing. Specifically, when temperatures decrease or increase, people tend to increase their demand for heating or cooling, respectively, leading directly to an uptick in energy consumption [16]. As a result, enterprises will increase the demand for carbon emission allowances, escalating carbon prices. ...
... (3) Weather factors. Several scholars have demonstrated the significant impact of weather variables, such as temperature, duration of sunshine, and precipitation, on carbon prices [16,19]. These weather factors mainly affect the carbon price by affecting energy consumption [33]. ...
Crude oil prices, weather changes, and carbon prices are closely related, but little research investigates their dynamic relationship. Studying how crude oil prices and weather changes dynamically affect carbon prices is significant to investors and producers. Taking carbon prices in Hubei and Guangdong as examples, we use the time-varying parameter vector autoregressive (TVP-VAR) model to investigate the time-varying influence of WTI and weather changes (average temperature, sunshine duration, and precipitation) on China's carbon prices. Our empirical results show that: firstly, the impact of crude oil prices and weather changes on carbon prices is obviously time-varying and lagging. Secondly, the short-term impact of crude oil prices and weather changes on carbon prices is higher than the long-term impact. And crude oil price mainly positively affects carbon prices in the short term. Finally, due to the differences in industrial development and weather conditions between Hubei and Guangdong, there are certain regional differences in the impact of crude oil prices and weather changes on carbon prices. Based on these research results, some suggestions are provided for global sustainable development and green transformation of enterprises. Funding for this research from National Social Science Foundation of China with ratification number 21BGL022 is gratefully acknowledged. We would like to thank the anonymous reviewers for their kind comments and valuable suggestions.
... Since most countries in the world have reached consensus on the topic of reducing carbon emissions, many countries have introduced relevant policies and evaluated their actual effectiveness in reducing carbon emissions or other aspects (Considine 2000;Zhang et al. 2020;Yang et al. 2021;Bousfield et al. 2022;Liu and Feng 2023;Zhu et al. 2023). Studies on carbon emission allowance prices have been conducted from many perspectives, such as pricing mechanisms, price forecasting, and price volatility effects (Abrell et al. 2022;Compernolle et al. 2022;Finch and van den Bergh 2022;Zhou et al. 2022a;Zhu et al. 2022a, b, c), and a large proportion of them are related to price determination factors (Rickels et al. 2007;Hao and Tian 2020;Zhao et al. 2021;Lu et al. 2023). ...
... The left y-axis of each market represents the extreme weather index values, and the right y-axis represents the carbon price values Table 2 Basic regression results weather t-1 is the first-order lag of extreme weather variable; price t-1 is the first-order lagged term after taking the logarithm of the carbon price; coal, oil, gas, air, gdp are the results of taking the logarithm of the current period values respectively. The standard error of each regression coefficient is shown in parentheses, the symbol * = 90%, ** 95% and ***99% confidence interval respectively (Considine 2000). ...
Extreme weather is an unexpected shock to the socioeconomic, which is likely to create climate risks in the process of global warming mitigation. The aim of this study is to investigate the impact of extreme weather on prices of China’s regional emission allowances, by using the panel data of four representative pilots in China (Beijing, Guangdong, Hubei, and Shanghai) from April 2014 to December 2020. The overall findings reveal that extreme weather, especially extreme heat, has a short-term lagged positive impact on carbon prices. In particular, the specific performance of extreme weather under different conditions is as follows: (i) carbon prices in tertiary-dominated markets are more sensitive to extreme weather, (ii) extreme heat has a positive effect on carbon prices while extreme cold does not, and (iii) the positive impact of extreme weather on carbon market is significantly stronger during compliance periods. This study provides the decision-making basis for emission traders to avoid losses caused by market fluctuations.
... A first general approach consists of estimating the demand for one or several energy goods based on an aggregate household model conditional on prices, income (or GDP) and climatic conditions (e.g. Narayan and Smyth, 2005;Hondroyiannis, 2004;Holtedahl and Joutz, 2004;Kamerschen and Porter, 2004;Considine, 2000 andGarcía, 2000). A second group of papers uses microeconomic data to estimate the demand for energy goods at the household level (e.g., Larsen and Nesbakken, 2004;Filippini and Pachauri, 2004;Oladosu, 2003;Leth-Petersen, 2002;Halvorsen and Larsen, 2001;Yatchew and No, 2001;Kayser, 2000;Vaage, 2000;Schmalensee and Stoker, 1999;Puller andGreening, 1999 andBaker et al., 1989) allowing for some additional explanatory variables as the stock of durable goods (heating systems, stock of electric appliances, etc.), housing (size, age of house, insulation, etc.) and household characteristics (number of members, age, income, etc.). ...
... Elasticity for LPG is larger than that for natural gas but much lower than that for electricity, which could be explained because the LPG share is extremely small for a large number of households. These results are similar to those found by Filippini (1995) and Halvorsen and Larsen (2001), thus contradicting the null effects estimated by Considine (2000) and García-Cerruti (2000). In fact, our own-price elasticity for electricity is within the average interval of estimates reported by the literature (see Narayan and Smyth, 2005), while our results for car fuels are close to Nicol's (2003) findings for the US. ...
... In our findings, the commercial and industrial sectors are the dominant drivers of energy demand increases. By considering a broader range of energy-using sectors, we highlight the potentially important impacts of commercial and industrial adaptation to climate change, which has only been explored for specific regions 4,[35][36][37][38] by few studies that confirm the potentially large impacts of these sectors. ...
... Despite regional variation in outcomes, we find a pervasive increase in the demand for electricity to satisfy increased cooling needs in multiple sectors. For Europe, a north-south gradient of impacts of opposing sign generates a median 2% net reduction in the total final energy, on par with the findings of fuel-specific 4,37,42 , is qualitatively in line with recent electricity-focused analyses [43][44][45][46] . Our disaggregated elasticities by sector and fuel also yield different results. ...
Future energy demand is likely to increase due to climate change, but the magnitude depends on many interacting sources of uncertainty. We combine econometrically estimated responses of energy use to income, hot and cold days with future projections of spatial population and national income under five socioeconomic scenarios and temperature increases around 2050 for two emission scenarios simulated by 21 Earth System Models (ESMs). Here we show that, across 210 realizations of socioeconomic and climate scenarios, vigorous (moderate) warming increases global climate-exposed energy demand before adaptation around 2050 by 25-58% (11-27%), on top of a factor 1.7-2.8 increase above present-day due to socioeconomic developments. We find broad agreement among ESMs that energy demand rises by more than 25% in the tropics and southern regions of the USA, Europe and China. Socioeconomic scenarios vary widely in the number of people in low-income countries exposed to increases in energy demand.
... The heating and cooling grades vary according to the regional changes during the days. Considine (1999), in his work with USA data, stated that electricity and natural gas demand is statistically sensitive to weather changes. ...
... Eskeland et al. Amato et al., (2005) noted that many studies have been conducted around the world for climate sensitivity (Quayle and Diaz, 1979;Le Comte and Warren, 1981;Warren and LeDuc, 1981;Downton et al., 1988;Badri, 1992;Lehman, 1994;Lam, 1998;Yan, 1998;Morris, 1999;Considine, 2000;Pardo, et al., 2002). ...
n modern society the problem of prostitution raises unless ethical questions and questions, related to the safety of prostitutes, the violation of their human rights, the activities of organized crime and the security of Bulgaria. Legislation in this area does not provide an adequate basis for effectively combating against prosperous of prostitution. This study focuses on the historical preconditions and reasons for the development of the prostitution as a common and multifaceted phenomenon. The trends and attitudes of the problem are monitored through the main psychological approaches. The social, the sociological and the legal aspects of the phenomenon are also considered. In the conducted experimental study, the impact of various factors on the psychological differentiation and functioning of prostitutes is monitored. As a consequence, the issue of the security of the individual and the whole Bulgarian society arises. International experience and possibilities to apply it in the Bulgarian institutions have been studied. The analysis of the prostitution management system has revealed some typical dependencies between the public significance of prostitution, its quantitative and qualitative features, officially announced goals of the state policy towards it, the adopted legislation and its relation with the national security of Bulgaria. These dependencies give rise to recommendations that would make it easier for the legislature to restrict prostitution in Bulgaria.
... Climate change can affect carbon price volatility through multiple channels. Earlier studies have shown that climate change can alter fossil energy consumption and thus affect carbon price fluctuations [57][58][59][60] . In the past few years, researchers have mainly addressed the significance of climate change on carbon prices from different perspectives. ...
It is essential to predict carbon prices precisely in order to reduce CO 2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model’s validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices.
... For example, Mu (2007) found that weather factors have a significant effect on both the conditional mean and volatility of natural gas futures returns. Considine (2000) examined the link between US natural gas consumption and weather, and showed that warm weather does reduce carbon emissions and natural gas consumption in the United States. Chan et al. (2009) analyzed the short-term demand role of weather indicators in the time-varying volatility of the natural gas market. ...
Weather has been shown to affect natural gas markets, but there is limited research on the strength and manner in which weather affects predictions of natural gas volatility. In this study, six weather indicators are used as exogenous variables, and seasonal‐trend decomposition‐generalized autoregressive conditional heteroskedasticity‐Weather (STL‐GARCH‐W) and STL‐GJR‐GARCH‐W models are constructed to explore the effect of weather on global natural gas market. The empirical findings indicate that temperature and precipitation have a notable positive effect on natural gas, while solar radiation has a prominent negative effect. Furthermore, the STL‐GARCH‐W model outperform the STL‐GJR‐GARCH‐W model and the benchmark STL‐GARCH model when temperature, precipitation, and solar radiation are considered. In addition, the January effect has been shown to significantly influence natural gas price volatility. Finally, most parameters in both models are of statistical significance, demonstrating that both models accurately forecast natural gas volatility and emphasizing the importance of weather indicators for modelling natural gas price volatility. Our study provides new insights for energy market investors and policy makers.
... In addition, it was pointed out that the energy industry is especially vulnerable to weather risks when considering that the energy demand is highly dependent on weather conditions. For instance, while the demand for gasoline and jet fuel is highly seasonal, it is not sensitive to temperature [32]. However, electricity, natural gas, and heating oil consumption are all sensitive to the weather. ...
This study empirically investigates the asymmetric effects of spot (future) prices and storage on rig counts in the US natural gas and crude oil markets from January 1986 to May 2020. It adopts the Nonlinear Autoregressive Distributed Lag (NARDL) model and establishes a flexible and efficient framework that measures the effects of positive and negative shocks in each of these variables on rig counts while modeling possible asymmetries in both the short and long term. For the natural gas market, the results reveal significant long-term asymmetric effects of spot (future) gas prices and storage on gas rigs. The positive and statistically significant cumulative effect of changes in natural gas storage suggests that larger natural gas storage has caused changes in the use of natural gas drilling rigs. For the crude oil market, we find significant short-term asymmetric effects of spot (future) gas prices and oil stocks on oil rigs. Furthermore, in addition to the optimal price and level of storage, the cost, as proxied by the interest rate, is a crucial determinant in rig drilling decision-making in the energy sector.
... This response is typically referred to as an intensive margin. Most early studies on intensive margins employed time-series data (Sailor and Muñoz 1997;Lam 1998;Considine 2000;Franco and Sanstad 2008). Notably, these studies varied in terms of the frequency of data and control variables. ...
Energy consumption is a chief contributor to climate change, which increases as households use more air conditioning (AC) in response to climate change. As such, climate change–induced energy consumption is expected to increase more drastically in fast-emerging economies, where the rapidly increasing household income and urbanization promote the large-scale adoption of ACs. Based on data on daily household electricity consumption in the Zhejiang Province of China, this study estimates the household temperature response functions. In particular, we consider urban and rural households with and without AC to chart their various cooling demand and consumption behavior, typically indicated by U-shaped temperature-response functions. Compared to rural households and those without AC, urban households and those with AC exhibit steeper response functions at both high and low temperatures. Based on these estimates, we simulate the household electricity consumption under climate change scenarios RCP4.5 and RCP8.5. The simulation results reveal that (1) under constant urbanization and AC adoption rates, the electricity consumption in the residential sector will increase by 5.04–16.37% because of climate change; (2) as the AC adoption rate increases from 82.50 to 95.00% in urban areas and from 74.40 to 85.00% in rural areas, the household electricity consumption in Zhejiang Province will further increase by 0.52–1.05%; (3) combined with the increase of urbanization from 68.73 to 80.00%, the increase rate of annual electricity consumption of the residential sector will further rise to 25.60–55.79%. These findings highlight the vicious cycle of climate change and cooling along with the challenges encountered by electricity grids.
... Weather conditions play a key role in energy demand; for instance, hot and humid summer conditions require more power for fans, air conditioners, irrigation and pumping of water [24][25][26]. A decline in the energy demand globally during the lockdown period of 2020 can be identified with the drop in worldwide financial activities and subsequent reduced use of energy sources such as petroleum products [27]. ...
The increasing population and its associated amenities demand innovative devices, infrastructure, methods, plans and policies. Regional climate has a great role in deciding the air quality and energy demand, and therefore, weather and climate have an indisputable role in its consumption and storage. Here, we present the changes in trace gases and associated regional weather in India during lockdown and unlock periods of COVID-19. We observe a reduction of about 30% in sulphur dioxide (SO2) and 10–20% in aerosols in the Indo-Gangetic Plain (IGP), large cities, industrial sites, mining areas and thermal power plants during lockdown as compared to the same period in the previous year and with respect to its climatology. However, a considerable increase in aerosols is found, particularly over IGP during Unlock 1.0 (1–30 June 2020), because of the relaxation of lockdown restrictions. The analyses also show a decrease in temperature by 1–3 °C during lockdown compared to its climatology for the same period, mainly in IGP and Central India, possibly due to the significant reduction in absorbing aerosols such as black carbon and decrease in humidity during the period. The west coast, northwest and central India show reduced wind speed when compared to its previous year and climatological values, suggesting that there was a change in regional weather due to the lockdown. Energy demand in India decreased by about 25–30% during the first phase of lockdown and about 20% during the complete lockdown period. This study thus suggests that the reduction of pollution could also modify local weather, and these results would be useful for drafting policy decisions on air pollution reduction, urban development, the energy sector, agriculture and water resources.
... In terms of macroeconomic development indicators, concerning scholars such as Guo and Zhou take the CSI 300 index as a representative indicator of economic development [15,16]. When used as a basis for evaluating investment performance, the CSI 300 index responds to the compilation goals and operational status of the CSI 300 index jointly announced by the Shanghai and Shenzhen Stock Exchanges on April 8, 2005. ...
As an efficient measure to deal with the intensification of the greenhouse effect, markets for carbon emission trading have been considered to establish by governments in developed countries. However, A stable price system has not yet developed for China's carbon trading market, which is still in its early stages. Therefore, to stabilize the price of carbon trading, enhance the carbon trading price system, and support the wholesome growth of the carbon trading market in China, it is essential to conduct research on the factors that affect carbon trading price. Based on the trading mechanism and the monthly data of the Beijing carbon trading market from 2014 to 2020, this paper investigates the influence patterns of energy price, industrial development, macroeconomic growth, and air quality on carbon price changes, and unit root test, Johansen cointegration test, and error correction model are used to analyze the patterns. The empirical results show that industrial development, economic growth, energy prices, and air quality have a long-run equilibrium relationship with carbon prices. While industrial development indicators are negatively correlated with the price of carbon, the price of energy, economic growth, and air quality are positively correlated with the price of carbon emission trading. Finally, some relevant development suggestions are proposed according to the empirical results.
... When the literature on energy demand and climate relationship is examined, Considine (2000) examined the effects of weather changes on demand for energy. He suggested that a significant portion of the total energy consumption is sensitive to short-run fluctuations in the climate or weather conditions. ...
This study examines the effects of climate and oil prices on residential natural gas prices in selected 11 OECD countries by using panel data for the period 1992-2016. After applying the panel unit root tests, the parameters are estimated using Common Correlated Effects Pooled (CCEP) method. Moreover, Emirmahmutoglu-Kose (2011) test is used to test the panel causality between the variables. The results revealed that in the long run, the heating degree days have a statistically significant and negative effect on natural gas prices used in the residential sector in selected OECD countries, while there is an insignificant relationship between oil prices and natural gas prices used in the residential sector in these countries. It is also found to be a causality of heating degree days to natural gas prices.
... According to the vast majority of literature now available, the energy-environmentgrowth nexus studies which use the simultaneous equation model have not considered that climate affects energy and the environment in many ways, although extreme temperature changes could distinctly affect energy consumption, and thus GHGs. For example, Considine [36] evaluated the driving factors of GHGs, and the results of the linear logit model indicated the impact of weather changes on GHGs is considerable. That is, the hot summer increases the demand for air conditioning and electricity, which in turn increases energy consumption. ...
With economic growth and rising incomes, increasing consumption of fossil energy is leading to environmental pollution and climate change, which requires increased innovative inputs to promote the efficiency of renewable energy use. Considering the important impact of innovation input and climate change on renewable energy consumption, greenhouse gas emissions, and green economic growth, this study uses simultaneous equation and sys-GMM model to explore the dynamic nexus of innovation input, climate change, and energy-environment-growth in OECD and non-OECD countries, with panel data covering 2000 to 2019. The empirical results show that renewable energy consumption in non-OECD countries significantly promoted green economic growth, while OECD countries did the opposite. Moreover, renewable energy consumption significantly reduces greenhouse gas emissions caused by climate change, especially for OECD countries. When the level of economic growth exceeds a certain inflection point, greenhouse gas emissions begin to turn from positive to negative, which further verifies the EKC hypothesis. In addition, this study found that innovation input has significantly increased renewable energy consumption, reduced greenhouse gas emissions, and promoted green economic growth in OECD countries. Finally, this study also found that the impact of innovation input in OECD and non-OECD countries on the energy-environment-growth nexus is greater in the short term and more significant in the medium and long term, while the impact of climate change on the energy-environment nexus in OECD and non-OECD countries is more significant in the medium and long term.
... Mansanet-Bataller [10] et al. showed that extreme weather conditions may affect the price of carbon emissions. Considine [11] argues that weather affects the price of carbon trading by influencing the demand for energy. The rationale is that extreme weather increases the demand for cooling or heating and thus has an impact on energy, while precipitation and wind speed have an influence on clean energy generation and thus on carbon pricing. ...
Currently, global warming became a world focus as its potential impacts on all human beings. To prevent the damages of greenhouse gas emissions, the carbon market established and operated for several years. In this paper, due to an important financial reality that the carbon market should be regarded as a common financial market, this paper briefly reviews the pricing factors of the novel carbon asset to benefit the market when it is time to price the carbon asset. And finally, this paper points out the potential future investigations.
... In the existing literature, most carbon price prediction is mainly based on the time series data of carbon price itself. However, a large number of studies have proved that carbon price is influenced by many factors, such as policy factors [2], weather conditions [3], and the energy market [4]. It is worth noting that the main cause of greenhouse gas production is still the burning of fossil energy. ...
With the global concern for carbon dioxide, the carbon emission trading market is becoming more and more important. An accurate forecast of carbon price plays a significant role in
understanding the dynamics of the carbon trading market and achieving national emission reduction
targets. Carbon prices are influenced by many factors, which makes carbon price forecasting a complicated problem. In recent years, deep learning models are widely used in price forecasting, because they have high forecasting accuracy when dealing with nonlinear time series data. In this paper, Multivariate Long Short-Term Memory (LSTM) in deep learning is used to forecast carbon prices in China, which takes into account the factors affecting the carbon price. The historical time series data of carbon prices in Hubei (HBEA) and Guangdong (GDEA) and three traditional energy prices affecting carbon prices from 5 May 2014 to 22 July 2021 are collected to form two data sets. To prove the forecast effect of our model, this paper not only uses Multivariate LSTM, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Network (RNN) to forecast the same data, but also compares the forecast results of Multivariate LSTM with the existing research on HBEA and GDEA forecast based on deep learning recently. The results show that the MAE, MSE, and RMSE obtained by the Multivariate LSTM are all smaller than other prediction models, which proves that
the model is more suitable for carbon price forecast and offers a new approach to carbon prices forecast. This research conclusion also provides some policy implications.
... Factors influencing natural gas price volatility include an increase in clean energy (Cadoret and Padovano, 2016;Kumar et al., 2017;Wan et al., 2021), crude oil price fluctuations (Krichene, 2002;Villar and Joutz, 2006;Regnard and Zakoian, 2011), weather changes (Considine, 2000;Yang et al., 2020), strengthened market supervision (MacAvoy, 2008;Apergis and Ozturk, 2015), and global commitments to low-carbon emissions and sustainable development (Janssen et al., 2006;Yang, 2018). Crude oil and natural gas prices, for example, are highly correlated (Asche and Misund, 2016;Geman, 2009), and weather changes induce demand shifts and price swings for natural gas (Elkhafif, 1996;Mu, 2007). ...
Environmental finance has gained considerable attention globally as an emerging interdisciplinary research area. This study uses bibliometric analysis to systematically review major studies on environmental finance-related areas published since the 1970s. Through a bibliometric analysis of 892 environmental finance-related articles sourced from the Web of Science database, we identified the main research streams and illustrated the trending research themes of environmental finance. We find that publications related to environmental finance have increased exponentially over the past decade. Current research streams include corporate and social responsibility (CSR), climate negotiations, natural gas price volatility, national policy, and cost comparisons. Further analysis of the recent five years of literature shows that emerging research topics include climate finance, sustainable finance, firm value, climate risk, and green bonds. Finally, we conclude with a future research agenda for environmental finance.
... Different empirical frameworks for evaluating the shocks on energy consumption due to varying weather conditions can be found in the literature [9]. Studies that adopt energy statistics aggregated to annual or monthly levels in a panel framework typically capture the elasticities of energy demand employing static models [5,10,11,12,13,14,28,29,30]. Static regression models constrain short-term elasticities of the energy response to weather to be stable over time. ...
... These analyses quantify how electricity demand responds to climate change and in general find a U-shaped relationship between temperature and electricity demand. Only a few analyses however have empirically evaluated climate impacts on total energy use that includes all types of primary energy sources, not just electricity as a secondary energy source (Sailor and Muñoz 1997, Considine 2000, De Cian et al. 2007, Mansur et al. 2008). ...
This paper presents a global analysis of the link between annual total energy use and temperature. A statistical model is used to estimate this link based on a panel dataset from 147 countries over the years 1990–2015. Results show that rich and poor countries exhibit differential response functions to temperature changes for annual total energy use. Unmitigated climate change by 2095 is projected to increase global total energy use on average by 24.0% relative to a baseline coupled with income and population growth without climate change. Poor countries are projected to face a larger increase in their energy use than rich countries over the years 2016–2095 and thus the projected impacts of future global warming on total energy use vary spatially—low-income countries will face significant increases, while cooler countries will experience reductions. Policy-makers need to incorporate socioeconomic factors and climate uncertainty into the projection of future climate change impacts on global energy use.
... The lastmentioned variable is highly affected by the geographical location of the analysis area. Possible types of impacts of the climate change on cooling and heating loads, being the key parameters in determining the energy consumption of buildings, have been widely discussed in several studies [54][55][56], many of which have been based on the degree-day approach [1,10]. Using computational methods, the majority of aforementioned studies predict a sharp decrease in the heating energy demand and a considerable increase in the cooling demand in the building sector [57,58]. ...
The growth of urban population as the result of economic and industrial development has changed our place of living from a prosperous place to where the resources are carelessly consumed. On the other hand, long-term climate change, i.e. global warming, has had adverse impact on our resources. Certain resources are on the verge of depletion as the consequence of climate change and inconsiderate consumption of resources, unless serious measures are implemented immediately. The building sector, whose share in the municipal energy consumption is considerably high, is a key player that may successfully solve the problem. This paper aims to study the effects of climate change on the energy consumption of buildings and analyze its magnitude to increase the awareness of how construction can reduce the overall global energy consumption. A descriptive-analytical method has been applied to analyze valid models of energy consumption according to different scenarios and to interpret the conditions underlying current and future energy consumption of buildings. The results clearly show that the energy consumption in the building sector increasingly depends on the cooling demand. With that being said, we can expect the reduction of overall energy consumption of buildings in regions with high heating demands, whereas rising the energy consumption in buildings is expected in regions with high cooling demand. To conclude, the long-term climate change (e.g. global warming) underlies the increased energy consumption for the cooling demand whose share in total energy consumption of buildings much outweighs the heating demand. Therefore, to conserve our resources, urban energy planning and management should focus on working up a proper framework of guidelines on how to mitigate the cooling loads in the energy consumption patterns of buildings.
... Some papers selected as their locus the interaction between residential energy demand and weather temperature found the presence of a heterogeneous relationship (Schlenker, Hanemann, and Fisher 2005). The growing demand for heating in cold winter and cooling during hot summer days justifies the findings of those studies (Considine 2000). ...
Recent evidence points to global warming and climate change as the biggest issues of the century; thus, the analysis of the weather-commodity futures prices relationship has crucial importance. This paper considers the relationship between weather anomalies, proxied by the Global Historical Surface Temperature Anomalies (HadCRUT4), and futures prices of agricultural products, energy commodities, industrial, and precious metals. Analyzing the monthly data between December 1982 and November 2020, the outcomes of the novel Granger causality test suggest unidirectional causality from the temperature anomalies to commodity futures prices. The findings imply that global temperature anomalies impact the expectations about the agricultural- and energy-related economic activities, including the use of commercial and organic fertilizers and fossil fuel combustion, respectively.
... Few studies have analyzed the correlation between HDDs and energy consumption. Considine [60] examined the impacts of climate fluctuations on US energy demand by analyzing the influence of weather changes on heating and cooling degree-days. The results suggest that a warmer climate in the US since 1982 has slightly reduced energy use associated with heating. ...
Effective building-energy policy can be developed only when the appropriate analysis is implemented in advance. The heating degree-day (HDD) method is a powerful tool for anticipating a national climate or annual heating demand for a heating period. The accuracy of the HDD method depends on the accuracy of balance-point temperatures, which represent regional building thermal performance and climate conditions. However, accurate local balance-point temperatures have not been determined for most countries.
This paper examines a method of calculating balance-point temperatures based on local building thermal performance and climate. Detailed calculation procedures for heat gains and losses in buildings are described and corresponding issues are discussed. Regional building thermal performance and climate are the main factors involved in the process, which requires appropriate regional balance-point temperatures. To reflect changes in climate, and subsequent policy changes, many countries will require more consideration of regional balance-point temperatures.
Balance-point temperatures were revised in Korea as a case study. The colder and the more intense the building insulation standards, the lower the balance-point temperature turned out to be, compared with what the American Society of Heating, Refrigerating and Air-Conditioning Engineers recommends. There was a 2 °C difference in balance-point temperature between the coldest and warmest regions in Korea. Insulation standards that demonstrated the greatest impact on balance-point temperature and severity of climate were also not negligible.
... The climatic sensitivity of electricity demand can be investigated through regressing electricity consumption on these indicators of temperature variations. Representative literature in this context includes Al-Zayer and Al-Ibrahim (1996), Considine (2000), Pardo, Meneu, and Valor (2002), and Ahmed et al. (2012). ...
It is widely accepted that energy use contributes to climate change; however, climate change can also affect energy demand. There is ample proof in the literature that a feedback phenomenon exists; however, empirical evidence of its mechanism and operation in different contexts is missing. As China is the largest consumer of electricity worldwide, a detailed study of its energy consumption patterns would be insightful; moreover, how the increasing income of Chinese residents affects the climate sensitivity of electricity demand is particularly relevant. Using data from 278 cities in China over the period 2005 to 2015, this study applies a newly developed technique, partially linear functional-coefficient panel data model, which enables disclosure of the role of income levels. The results indicate that climate change significantly stimulates residential electricity consumption in hot weather rather than in cold weather. Additionally, the level of income affects climate sensitivity. Specifically, an increase in income initially increases the marginal effect of cooling degree days (days on which building cooling is desired) on electricity consumption, but the curve of the marginal increment becomes flat as income growth increases further.
... For Kuwait, the diffusion rate is higher due to the high levels of income and electrification rate. Several researchers have analyzed the effect of weather on energy consumption [52][53][54]. For the case of Kuwait, and based on [55][56][57], the influence of weather in the form of cooling degree-days (CDD) on long-term electricity demand forecasting is only statistically significant at 20% due to the low year-to-year weather variation in Kuwait. ...
To meet the rapid-growing demand for electricity in Kuwait, utility planners need to be informed on the energy consumption to implement energy efficiency measures to manage sustainable load growth and avoid the high costs of increasing generation capacities. The first step of forecasting the future energy profile is to establish a baseline for Kuwait (i.e., a business-as-usual reference scenario where no energy efficiency incentives were given and the adoption of energy efficient equipment is purely market-driven). This paper presents an investigation of creating a baseline end-use energy profile until 2040 for the residential sector in Kuwait by using a bottom-up approach. The forecast consists of mainly two steps: (1) Forecasting the quantity of the residential energy-consuming equipment in the entire sector until 2040 where this paper used a stock-and-flow model that accounted for the income level, electrification, and urbanization rate to predict the quantify of the equipment over the years until 2040, and (2) calculate the unit energy consumption (UEC) for all equipment types using a variety of methods including EnergyPlus simulation models for cooling equipment. By combining the unit energy consumption and quantity of the equipment over the years, this paper established a baseline energy use profile for different end-use equipment for Kuwait until 2040. The results showed that the air conditioning loads accounted for 67% of residential electrical consumption and 72% of residential peak demand in Kuwait. The highest energy consuming appliances were refrigerators and freezers. Additionally, the air conditioning loads are expected to rise in the future, with an average annual growth rate of 2.9%, whereas the lighting and water heating loads are expected to rise at a much lower rate.
... First, some studies use monthly average temperature-the simplest approach-as a measure of temperature effects (e.g., [15,20]). A second set of studies adopt monthly or yearly cooling and heating degree days rather than using average temperature (e.g., [21,22]). However, these two measures can hide extreme events in both cold and hot weather and cannot reflect the nonlinear relationship between economic outcome and weather. ...
Climate change is around us today and will affect human life in many ways. More frequent extreme weather events raise mortality and car accident rates, global warming leads to longer growing seasons for crops, which may change farmers’ crop choices, and the relationship between energy demand in residential buildings and weather is widely investigated. In this paper, we focus on the impact of weather on energy consumption, in particular, gasoline consumption through the more frequent use of both vehicles themselves and the air conditioner of the vehicle that decreases fuel economy, which has not been paid enough attention in the literature. We estimate the relationship between fuel consumption and weather using unique U.S. panel data. We find that hot days increase gasoline consumption, but in contrast to the results of residential energy consumption literature, there is no statistically significant effect on cold weather. With climate prediction data from General Circulation Models (GCMs), we simulate the impact of climate change on fuel energy consumption. The results show that the fuel consumption in the transportation sector may increase by up to 4% under the “business-as-usual” (RCP 8.5) scenario. Also, climate change has heterogeneous impacts across the continental United States.
... We are unable to find rigorous estimates of the price elasticity of demand for propane in the Upper Peninsula. However, Considine (2000) finds energy consumers have highly inelastic demand for other fuel sources, including natural gas and heating oil. This supports our assumption that propane consumption will be largely insensitive to changes in fuel price. ...
This paper presents research on the economic damages from a hypothetical worst-case oil spill at the Straits of Mackinac between Lakes Huron and Michigan. This spill could occur because the Enbridge Line 5 oil pipeline traverses the Straits between Michigan’s Upper and Lower Peninsula. We quantify potential economic damages to outdoor recreation, commercial fishing, shipping, residential properties, and energy and water supplies. Damages are estimated for two spill scenarios occurring at the onset of the summer tourism season with extensive shoreline oiling. Using evidence from past spills, economic damages would last for between one and two years and would affect locations on the periphery of the spill area, depending on the activity. We project the loss from the worst-case scenario would be at least $1.3 billion.
In response to the potential green swans event in the future, China is adopting market‐oriented means to encourage green development, specifically through carbon emission trading schemes. At the same time, under the outbreak of the current global pandemic, it is equally important to consider the impact of black swan events. Therefore, this study aims to analyse the fluctuations in carbon emission trading prices under green and black swan events by utilizing daily data from seven carbon emission exchanges in China from 2017 to 2020. The analysis includes the construction of multiple regression models, PVAR models, and panel threshold models. Additionally, the study addresses the endogeneity problem by using instrumental variables. The findings of the study indicate that: (1) Rising temperatures will drive up the carbon emissions trading price, and this impact will persist over time. On the other hand, increased humidity levels and sunshine hours will reduce the carbon emissions trading price. Furthermore, there is a positive correlation between the increase in the price of primary energy and the increase in the carbon emissions trading price. (2) The spread of COVID‐19 has a restraining effect on the increase in temperature and will have a long‐term negative impact on the carbon emissions trading price. (3) The threshold effect concerning the prevalence of pandemics is recognized, which implies that the impact of the epidemic is staged and nonlinear. Overall, the results of this article highlight the importance of a reasonable response to both black swan and green swan events in order to enhance the efficiency of the current emission trading scheme.
This study explores the predictive effect of climate change attention on carbon futures returns. Using climate‐related Google Trends and news, we construct five dimensions of the public climate attention index and media climate attention index. After feature selection, we incorporate the optimized combination with lagged order into the machine learning model to predict EU Emission Allowance futures returns. Our empirical results show that the forecasting models with climate attention outperform the corresponding benchmark models, indicating that climate attention does provide predictive information for carbon futures returns. In addition, we carry out trading simulations to investigate the economic performance of the forecast results. It turns out that the market strategies based on the prediction models with climate attention can deliver more benefits than the counterpart market strategies. More specifically, the cumulative returns reach 140% during the out‐of‐sample period, much higher than 79% of the cumulative returns of the buy‐and‐hold strategy.
The main aim of this paper is to diagnose the impact of climate change with the analysis of its scenario, causes and its solutions in relation to energy sector. Initially, this paper discusses the scenario of climate change in India. Afterward, a comparative difference analysis of selected climatic variables is performed on the chosen areas and also determines the relationships among these variables. Both these objectives are performed by ANOVA, post-hoc, correlation and linear regression analysis. Subsequently, the forecasting of several climatic variables is performed from 2021 to 2025 by time series analysis utilizing the ARIMA (Autoregressive Integrated Moving Average) model. Then, this paper investigates the interdependency scenario between the climate change and the electricity sector. Afterward, this paper discusses some major impacts of climate change on the electricity sector. Then, this paper presents the present electricity scenario of India and the renewable power potential of India. Subsequently, this paper suggests some open research challenges with recommendations for improving the energy sector of India. Results of this diagnoses analysis concludes that the climate change and energy sector are mutually connected, and the climate change impacts can be reduced with the improvement in energy sector using renewable energy.
This study aims to analyzes the predictability of the natural gas volatility by considering extreme weather information. Based on extended GARCH-MIDAS models, empirical results show that the predictive model adding weather indicators can indeed outperform the model without weather indicators. Importantly, some extreme weather indicators can provide more valuable information to predict the natural gas volatility based on the various out-of-sample tests. Our new weather-related GARCH-MIDAS-ES model can exhibit a new insight on the natural gas volatility forecasting.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
There are often blanket claims that the world is facing more problems than ever but there is a lack of empirical data to show where things have deteriorated or in fact improved. In this book, some of the world's leading economists discuss ten problems that have blighted human development, ranging from malnutrition, education, and climate change, to trade barriers and armed conflicts. Costs of the problems are quantified in percent of GDP, giving readers a unique opportunity to understand the development of each problem over the past century and the likely development into the middle of this century, and to compare the size of the challenges. For example: how bad was air pollution in 1900? How has it deteriorated and what about the future? Did climate change cost more than malnutrition in 2010? This pioneering initiative to provide answers to many of these questions will undoubtedly spark debate amongst a wide readership.
Revealing the trends and main drivers of rural energy transition has important implications for building up a clean, efficient and sustainable energy system. Based on physical energy consumption, we measure the effective energy consumption and constructed a provincial panel dataset of rural China from 1990 to 2017. The results show that the energy transition is not only reflected in the growth of fuel consumption and the change of fuel structure, especially the growth of high-quality energy, the diversification of service functions and the convenience of use. The term of effective energy can more accurately measure the level of energy consumption per capita than physical energy. The income per capita, urbanization level and annual average temperature are three main factors impacting energy consumption. Every 1,000 yuan growth in per capita income can increase the consumption of 6 kgce physical energy and 4 kgce effective energy, respectively. Every 1% rise in the urbanization rate of the population can bring a growth of 4.86 kgce physical energy and 1.83 kgce effective energy. 1℃ of rise in average temperature may decrease by 22.3 kgce physical energy and 2.8 kgce effective energy. There are roughly-three levels of transition, the slow transition in the northeast, the forefront transition in the eastern coastal areas and the intermediate level transition in vast central and western regions. According to regional differences, focus should be on increasing the share of renewable energy, improving energy infrastructure and energy efficiency in the future.
Carbon trading is a vital market mechanism to achieve carbon emission reduction. The accurate prediction of the carbon price is conducive to the effective management and decision-making of the carbon trading market. However, existing research on carbon price forecasting has ignored the impacts of multiple factors on the carbon price, especially climate change. This study proposes a text-based framework for carbon price prediction that considers the impact of climate change. Textual online news is innovatively employed to construct a climate-related variable. The information is combined with other variables affecting the carbon price to forecast the carbon price, using a long short-term memory network and random forest model. The results demonstrate that the prediction accuracy of the carbon price in the Hubei and Guangdong carbon markets is enhanced by adding the textual variable that measures climate change.
Hydro and wind are both commonly deployed renewable sources of electricity. Wind is intermittent and unpredictable to a large degree. Hydro depends on seasonal rainfall patterns, although storage may offer scope to spread the risk associated with dry seasons. We investigate the impact of intermittent wind generation, coupled with a given hydro capacity, on wholesale electricity prices, accounting for both spatial and seasonal effects. Results from a spatial Durbin model provide evidence of significant negative spill-over effects of wind generation on wholesale nodal prices. After robustness analysis with alternative spatial weight matrices, we provide estimates of the seasonal impact of wind generation on nodal prices using a transmission weight matrix. We evaluate seasonal nodal price effects during dry periods and wet periods in New Zealand's hydro-dominated electricity market. Results show that the price reduction associated with a 10% increase in wind penetration varies across seasons, ranging from $0.48 per MWh in winter in 2012 to $3.05 per MWh in spring. The largest price reduction effect of 3.05 is found for the wet season in spring 2012, but the variance change is 3.6 which is less than 5.24 for the dry season in summer 2012. This evidence reveals that increased wind generation reduces nodal prices, as expected, but also increases the variance in nodal prices, particularly during a dry season. Thus, while a wind-hydro system is shown as a favourable low-carbon combination during wet seasons, without backup generation the system remains susceptible to price volatility. We further suggest policy recommendations to ensure the reduction of price volatility and the continuity of electricity supply during dry periods.
Public attention on natural gas price, which reflects the demand dynamics, is considered as a new factor to influence the movement of price. So investigate the impact of public attention on natural gas price is an innovative research issue in energy economics. This paper innovatively constructs a measure of public attention and examines its impact on natural gas price. A data set generated from Google Trends is used to measure public attention and then rigorous econometric models are applied to evaluate its predictive ability. The empirical study shows that (i) public attention is closely related to natural gas price, with contemporaneous positive correlation coefficient being 0.59, (ii) public attention leads natural gas price, (iii) the model including public attention data outperforms benchmark model. By using a more direct and representative way of forecasting based on the knowledge collected from the users, this paper also has important implications for applying Internet knowledge to improve the forecast accuracy of other energy price.
合理规划天然气供给系统是天然气市场调节的重要组成部分，有利于推动天然气行业供给侧改革，实现天然气产业可持续发展。天然气供给系统与天然气价格及需求紧密相关，通过对国内外相关研究成果进行调研，总结了全球范围内天然气价格波动的长期及短期影响因素，归纳了天然气需求量波动影响因素及预测方法。详述了近年来天然气市场多主体分析的主要研究成果，并依据输入参数类型梳理了国内外天然气供给链系统规划模型的最新进展。最后结合天然气市场调节系列问题的研究现状，提出了从供给侧出发的天然气市场调节思路，以期对中国天然气市场有效调节提供参考。
How to effectively manage risk is an important issue that the financial and commodity industries face. One of the issues is the estimation of the financial and consumption asset price volatility and estimation of the optimal hedge ratio. Our study examines whether it is important to incorporate fundamental variables in estimating price returns and volatilities by studying the U.S. natural gas market. In doing so, we explain the spot and futures returns and volatilities based on market fundamental variables such as weather, gas underground storage, oil price and macroeconomic news. We find significant impacts of most of these variables on gas price. In addition, we calculate the optimal hedge ratio based on the price and volatility estimations. Our empirical evidence suggests that, as expected, the optimal hedge ratio was not constant but fluctuated significantly during the sample period. Incorporating time-varying hedge ratio has improved hedging effectiveness by a large percentage. In addition, incorporating market fundamental variables further improves the hedging effectiveness significantly. Our empirical results support the proposition that it is important to incorporate fundamental market variables in analyzing commodity price movement and improving hedging effectiveness.
This comprehensive and up-to-date book explains the economic rationale behind the production, delivery and exchange of electricity. Cretì and Fontini explain why electricity markets exist, outlining the economic principles behind the exchange and supply of power to consumers and firms. They identify the specificities of electricity, as compared to other goods, and furthermore suggest how markets should be optimally designed to produce and deliver electricity effectively and efficiently. The authors also address key issues, including how electricity can be decarbonized. Written in a technical yet accessible style, this book will appeal to readers studying power system economics and the economics of electricity, as well as those more generally interested in energy economics, including engineering and management students looking to gain an understanding of electricity market analysis.
All but one of the U.S. recessions since World War II have been preceded, typically with a lag of around three-fourths of a year, by a dramatic increase in the price of crude petroleum. This does not mean that oil shocks caused these recessions. Evidence is presented, however, that even over the period 1948-72 this correlation is statistically significant and nonspurlious, supporting the proposition that oil shocks were a contributing factor in at least some of the U.S. recessions prior to 1972. By extension, energy price increases may account for much of post-OPEC macroeconomic performance.
It is sometimes assumed that the R2 of a regression of a first-order differenced time series on seasonal dummy variables reflects the amount of seasonal fluctuations that can be explained by deterministic variation in the series. In this paper we show that neglecting the presence of seasonal unit roots may yield spuriously high values of this coefficient.
The paper develops a two-step estimator for use in rational-expectations models with autocorrelated residuals and predetermined, but not strictly exogenous, instruments. The estimator extends the applicability of McCallum's (1976) error-in-variablesapproach to estimating such models, and is asymptotically efficient in a class of intrumental-variables estimators. As an application we use instrumental-variables techniques to estimate Taylor's (1979) rational-expectations macroeconomic model of the United States.
This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation.
This paper presents a theoretically consistent and tractable two-stage decision-making model that does not rely on the restrictive assumption that group aggregator functions are homothetic. By assuming that within-group cost functions satisfy the almost ideal demand system form, the model incorporates flexible expenditure effects, yet allows within-group prices to be aggregated into two indices that fully capture movements in those prices that are relevant for determining group demands. Thus, the model provides a more flexible alternative to the standard two-stage budgeting models. An empirical application to U.S. manufacturing demands, including an energy aggregate, is provided.
The Multinomial Logit (MNL) framework has been used in the agricultural production economics literature to model acreage share choices, crop decisions or land use decisions. This article extends the pioneering works of Caswell and Zilberman (1985) and of Wu and Segerson (1995) by developing further the theoretical background of the MNL acreage share models. Two approaches are considered: the “cost function approach” and the “discrete choice approach”. It is then shown that MNL acreage share models can be used to define simple multi-crop econometric models with land as an allocatable fixed input. Finally several generalizations of the standard MNL acreage share model are proposed.
We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity
and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our
procedure is asymptotically equivalent to one that is optimal under a mean-squared error loss function. Monte Carlo simulations
suggest that our procedure performs tolerably well, although it does result in size distortions.
This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
This paper describes a method for estimating and testing nonlinear rational expectations models directly from stochastic Euler equations. The estimation procedure makes sample counterparts to the population orthogonality conditions implied by the economic model close to zero. An attractive feature of this method is that the parameters of the dynamic objective functions of economic agents can be estimated without explicitly solving for the stochastic equilibrium.
Demand for natural gas: residential and commercial markets in Ontario and British Columbia. This paper reports on an attempt to estimate an econometric model for natural gas demand in the residential and commercial market sectors. The model specification is a generalization of one developed by Balestra and Nerlove. Demand that is potentially variable because it is not committed to past investments (`flexible demand') is distinguished from demand that is inflexible because it is tied to existing equipment stocks (`captive demand'). Specific attention is devoted to the effect of temperature variations on gas demand. The non-linear equation system is estimated by a maximum likelihood method, using annual data for British Columbia and Ontario 1959-74. /// La demande pour le gas naturel: les marchés des secteurs résidentiels et commerciaux en Ontario et en Colombie britannique. Ce mémoire présente une tentative pour estimer un modèle économétrique de la demande pour le gas naturel dans les marchés résidentiels et commerciaux. La spécification du modèle s'inspire de celle développée par P. Balestra et M. Nerlove qu'elle veut généraliser. On y distingue la demande flexible, potentiellement variable parce que non liée aux investissements passés, de la demande captive qui elle est liée aux équipements existants. On porte une attention particulière aux effets de la variation de la température sur la demande de gas naturel et les auteurs présentent une formulation générale qui permet de vérifier certaines hypothèse concurrentes. Des fonctions de demande séparées sont spécifiées pour l'Ontario et la Colombie britannique. Ces équations mélangent les variables dans une forme additive et multiplicative; de plus les équations sont non-linéaires dans leurs paramètres. Utilisant des données annuelles, le système des deux équations est estimé par la méthode du maximum de vraisemblance. Les résultats confirment l'hypothèse de l'importance significative des stocks d'équipement sur la demande de gas naturel tant pour le secteur résidentiel que pour le secteur commercial. Les variations de prix ont donc peu d'effet sur la demande à court terme; elles ont un effet plus important à long terme. L'élasticité de la demande court-terme par rapport au prix relatif est de l'ordre de -0.15, l'élasticité de la demande long-term de -0.69. La demande est inélastique par rapport au revenu et, par rapport au niveau de la température, l'élasticité de la demande est de 0.75 Les auteurs illustrent certaines utilisations de leur modèle pour la simulation de politiques à court terme. Ils suggèrent qu'une réduction de 10 pour cent dans le prix relatif du gas naturel depuis son niveau antérieur accroîtrait la consommation dans les deux provinces d'à peu près 5 pour cent au bout de la septième année.
This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms. Currently available estimators that are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme. No results are available regarding the choice of lag truncation parameter for a fixed sample size, regarding data-dependent automatic lag truncation parameters, or regarding the choice of weighting scheme. This paper addresses these problems. Asymptotically optimal kernel/weighting scheme and bandwidth/lag truncation parameters are obtained. Using these results, data-dependent automatic bandwidth/lag truncation parameters are introduced. Copyright 1991 by The Econometric Society.
This article addresses two problems with earlier applications of the linear logit model of cost shares. The first problem is that symmetry holds only for one set of cost shares. In this article, an iterative, nonlinear estimation procedure is used to impose symmetry for all predicted shares. The second problem is that analytical expressions for the integrals of log-linear cost shares cannot be derived analytically, although they exist, given the continuity of the logit share equations. This article proposes a procedure for approximating the underlying cost function by using the share predictions as stochastic, numerical approximations of these integrals. The model has constant but unequal elasticities of substitution. This feature does not contradict the Uzawa impossibility theorem, which holds for integrable functions, because the logit model is essentially a numerical approximation of the constant elasticity of substitution model. Another interesting property is that the concavity conditions are relatively stable functions of the cost shares, which remain positive given the logistic form.
Markup pricing in a short-run model with inventories
- T J Considine
Considine, T.J., 1999. Markup pricing in a short-run model with inventories, International Society for Inventory Research, invited paper, ASSA Meetings in Boston, MA., January 2000.