No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
The Impact of Climate Change on Energy Demand: A Dynamic Panel Analysis
Abstract
This paper presents an empirical study of energy demand, in which demand for a series of energy goods (Gas, Oil Products, Coal, Electricity) is expressed as a function of various factors, including temperature. Parameter values are estimated econometrically, using a dynamic panel data approach. Unlike previous studies in this field, the data sample has a global coverage, and special emphasis is given to the dynamic nature of demand, as well as to interactions between income levels and sensitivity to temperature variations. These features make the model results especially valuable in the analysis of climate change impacts. Results are interpreted in terms of derived demand for heating and cooling. Non-linearities and discontinuities emerge, making it necessary to distinguish between different countries, seasons, and energy sources. Short- and long-run temperature elasticities of demand are estimated.
... Specifically they considered monthly data from April 1989 to March 1990 and found a strong positive correlation between extreme temperatures and residential energy demand. De Cian et al. (2007) focused on the effect of energy prices, energy quantities, income levels represented with GDP and heating and cooling effects 1 in residential energy demand from 1978 to 2000, for 31 countries of the world. Adopting the Arellano and Bond (1991) procedure De Cian et al. (2007) found that the effect of temperature increases could be grouped into three groups of countries: cold countries such as Canada or Norway were the only ones where the temperature increases on total residential energy demand had a negative impact; in mild countries, such as Italy, the higher demand for residential electricity during the summer was compensated by lower demand for gas, oil products and coal in winter and spring; in warm countries, such as Mexico, the cooling effect increased energy demand not only in the summer, but also in the spring. ...
... De Cian et al. (2007) focused on the effect of energy prices, energy quantities, income levels represented with GDP and heating and cooling effects 1 in residential energy demand from 1978 to 2000, for 31 countries of the world. Adopting the Arellano and Bond (1991) procedure De Cian et al. (2007) found that the effect of temperature increases could be grouped into three groups of countries: cold countries such as Canada or Norway were the only ones where the temperature increases on total residential energy demand had a negative impact; in mild countries, such as Italy, the higher demand for residential electricity during the summer was compensated by lower demand for gas, oil products and coal in winter and spring; in warm countries, such as Mexico, the cooling effect increased energy demand not only in the summer, but also in the spring. All the aforementioned studies have dealt with relative short time periods and have focused either on forecasting future energy and gas consumption trends or providing estimations of the impact of temperature changes in the residential energy demand. ...
... As mentioned above, the focus is on the residential sector. Since an extensive literature (Berrisford, 1965; Sailor and Muñoz, 1997; De Cian et al., 2007) shows that the natural gas and total energy consumption trends may be depending on the temperature level, i.e. an increase of the temperature may indeed affect gas and energy consumption quantities. However the inclusion of the temperature effect creates not negligible issues into the decomposition analysis. ...
The International Energy Agency (IEA) calculates that US gas consumption per resident declined by 44.31% over the period 1970-2012. Differently, EU28 total per capita residential gas consumption incremented by 35% over the same period. By making use of the Logarithmic Mean Divisia Index (LMDI) we decompose the variations of residential gas consumption into five drivers: an activity effect, an economic effect, a gas intensity of energy effect, an energy intensity weather adjusted effect, and a temperature effect. Unlike previous studies we focus on the role of temperature in explaining the variations of residential gas consumption over time. The results suggest that household disposable income per capita and population play a positive role in explaining the upwards variations of residential gas consumption for the USA while the greater intensity of gas with respect to the total energy consumption is the main driver to positively affect residential gas consumption for the european countries analyzed. Moreover the effect of temperature in the US residential gas consumption is only slightly negative, while for the european countries the negative role of temperature on residential gas consumption is more evident. This last result proves that for the european economies analysed energy intensity improvements have been in part the result of warmer temperatures. We conclude that not adjusting for the increasing temperature overestimates the role of energy efficiency improvements.
... However, the use of energy is not only in space cooling or heating, a series of energy products (coal, oil, natural gas, electricity, etc.) are also widely used in all aspects of economic production, lighting, cooking and other aspects. Hence, it is not only climate change that drives the change of energy demand, but also regional economic development, because economic development will affect the ability to adapt to climate change (De Cian et al., 2007;Hasegawa et al., 2016;van Ruijven et al., 2019). China accounts for 18% of the world's primary energy consumption (Li, 2012). ...
... EDD model. Energy demand is modeled as an autoregressive process, depending on its own lagged values and a set of independent variables (De Cian et al., 2007). In this set of independent variables, HDDs and CDDs are the climate change variables, and per capita GDP representing economic development variables is included. ...
The energy demand is significantly affected by climate change and economic factors, and the distribution of the demand among different regions in China is unequal because of the large regional differences in climate and economic development. Moreover, demand changes will directly affect the energy sector as the supply side amplifies the economic impact through the industrial linkages among regions and sectors. Therefore, we developed an integrated assessment model combining an energy demand-driven model (EDD) and adaptive multiregional input-output model (AMRIO). The assumptions were used in the model based on China's future climate, economy and energy development. In addition, we evaluated the century-scale energy demand change and its possible economic impact in 5 future scenarios (SSPx-y of CMIP6). The results show that the following: (i) The energy demand in China will continue to increase by approximately 1.89 to 1.94 times by 2100, resulting in a negative economic impact on GDP ranging from 8.19% to 12.05%. (ii) The energy sector will cumulatively benefit by 26% because of the increase in demand; however, other sectors will suffer negative economic ripple impacts due to the imbalance of supply and demand, especially manufacturing (719%) and agriculture (577%). The results provide quantitative information for policymakers to fully consider the positive and negative impacts of future energy change when making climate mitigation strategies.
... The demand is mainly interesting the residential and the transport sector, meanwhile the industrial sector is relatively smaller compared to the neighboring countries. The electricity is generated mainly through hydropower plants, so the share of the renewable energies over the total is higher than the other countries under analysis (De Cian et al., 2007;Zogolli, 2015). The gap in the power supply is simply filled by unplanned and unpredicted outages. ...
... Important to note that energy demand in the inner continental areas is more dependent on weather conditions rather than disposal income. It is commonly known that the energy demand has a low elasticity coefficient in colder countries during winter seasons (De Cian, Lanzi, & Roson, 2007), meanwhile they benefit from milder mid-seasons and, instead, are not significantly sensitive to summer temperatures (Avdakovic, Ademovic, & Nuhanovic, 2013). ...
This paper investigates the relationships between energy consumption and GDP growth for 6 Western Balkan countries over 10 years period from 2005 to 2014. The countries under consideration are: Albania, Bosnia and Herzegovina, Croatia, Serbia, Montenegro, and Macedonia, FYR. The aim of this study is to evaluate the energy demand across time and within these countries. The other variables that are considered in the model are the Electricity use per capita, the Oil price referred to Crude Oil International markets price expressed in USD and the exchange rate. Recently, numerous empirical studies have been conducted to detect this relationship, but not specifically to the Western Balkan region. There are general characteristics, due to the common historical background, but also specific patterns of the economic structure shaping the energy demand of each country. The main approaches to energy demand modeling are the Bottom-up and Top Down approaches. Currently important research is conveying also toward the Hybrid models. The demand in this countries is very susceptible to external oscillations, leading to severe exogenous impacts on the long term equilibrium, fitting more towards a top down macroeconometric model.
... More compounding is the issue of climate change and seasonal variations which can cause peaking of energy demand in a particular region (Ahmed et al., 2012;De Cian et al., 2007;Head et al., 2014). Therefore, understanding the influence of climatic factors on energy demand and how the seasonal variations will affect energy planning is necessary for Australia. ...
... This results somewhat differ from Ahmed et al. (2012) study where the population was observed to influence electricity increase during the summer and autumn. However, this may be due to the consumption of other fuels such as natural gas which are significantly higher during the winter season (De Cian et al., 2007;Directorate, 2015). In QLD, population growth only influenced energy demand during the spring season. ...
... services (Bigano et al., 2008); changes in health care expenditure are translated into changes in the public and private demand for the " non market services " sector, which includes health services (Bosello et al., 2006); changes in regional demand for oil, gas and electricity are modelled as changes in the demand for the output of the respective industries (De Cian et al., 2007). Changes in net forest productivity are derived from Songhen et al. (2001) As can be clearly seen, impacts differ greatly from region to region and type to type. ...
... Climate-change impacts on energy demand are derived from De Cian et al. (2007). This study estimates household energy demand on a macro dynamic panel dataset spanning from 1978 to 2000, for 31 countries. ...
The present study integrates Computable General Equilibrium (CGE) modelling with biodiversity services, proposing a possible methodology for assessing climate-change impacts on ecosystems. The assessment focuses on climate change impacts on carbon sequestration services provided by European forest, cropland and grassland ecosystems and on provisioning services, but provided by forest and cropland ecosystems only. To do this via a CGE model it is necessary to identify first the role that these ecosystem services play in marketable transactions; then how climate change can impact these services; and finally how the economic system reacts to those changes by adjusting demand and supply across sectors, domestically and internationally.
... While their work is not directly related to retail sales, it emphasizes the importance of considering climate change in our analysis. In all the studies cited, authors account for climate change, such as [11] (not cited in the survey paper); as the data we have are over two years, we only need to account for climate. In addition, we consider the retail sales of items that are far less essential than electricity that is used for heating, cooling, cooking, and lighting. ...
In this paper, we explore the importance of accounting for climate when determining the impact of weather on product sales. Using a France-wide scanner panel dataset provided by our industry partner, we show that if climate is not accounted for, product categories may be misclassified as being weather sensitive when they are not, and vice versa. This is motivated by previous research and industry reports that suggest a relationship between weather and retail sales. However, these studies often fail to distinguish between weather and climate, leading to inaccurate conclusions. Our results highlight the need to control for climate in order to accurately assess the effects of weather on retail sales. We use ordinary least squares regression to estimate the relationship between temperature and sales for 29 different product categories. The regression models control for various factors, including shelf space allocation, week of observation, quantity purchased, promotion, store brand, store surface area, store competition, and consumer behavior measures. We find that when accounting for climate, only a subset of the product categories is sensitive to weather. Additionally, we show that climate can be approximated using a week index, eliminating the need for additional data collection and approximation efforts. Our findings have implications for both researchers and practitioners. Researchers should be aware of the importance of accounting for climate when studying the impact of weather on retail sales, as failing to do so may lead to erroneous conclusions. Practitioners can use our results to inform their marketing and sales strategies, taking into account the weather sensitivity of different product categories and the role of climate in shaping consumer behavior. Overall, our study emphasizes the need to consider climate when determining the impact of weather on retail sales, and provides practical insights for retailers and economists.
... The seasonal energy and electricity consumption pattern typically exhibit two peaks, one in the winter and the other in the summer, with the summer peak in recent years becoming progressively higher. The impact of rising or lowering temperatures on annual energy demand varies by region [33]. Some studies show that when the temperatures rise above 30 o C, demand increases by about 11% [11]. ...
The paper introduces a statistical model that connects the electrical demand in Jordan with several determinants that have a direct impact on the electrical consumption and load profile during the study period from 2007 to 2020. The period was selected as it is characterized by several global events that directly impacted Jordan’s economy and energy sustainability in Jordan, such as the Arab spring protests, the civil war in Syria, and the global financial crises. Many determinants that are used in the regression analysis imply the ambient temperature, day of the week, population, gross domestic product (GDP), oil price, and technological factors related to renewable energy projects. Results show that temperature and population positively impact the demand, whereas GPD, population, oil prices, and renewable energy negatively impact the electricity demand. The results obtained from backcasting regression analysis for the hourly 4745 data set covering 13 years period reveals reasonable error metrics with MAE, MAPE, and RMSE values of 134, 6.3% and 2.76%, respectively. The government must encourage investments to exploit and explore the massive potential of available energy resources such as oil, natural gas, oil shale, and uranium to resolve the problems related to the high global oil prices and high dependency on imported energy. Also, it is required to enable the transition from fossil fuels to renewable energy through financial incentives and tax exemption to encourage investments in clean energy, rebuild a new traffic system showing the volatile electricity prices, which are still unknown and finally remove obstacles and facilitate the ongoing projects, reaching a state of stakeholder buy-in engaging with the projects.
... The use of electricity demand as an energy indicator is not a totally rigorous assumption, because other factors such as the use of heating or cooling systems also contribute to demand (Botzen Wouter et al., 2021;De Cian et al., 2007). The use of those systems is directly related to temperature; for this reason (in contrast to Guevara et al. (2020aGuevara et al. ( , 2020b, who used machine learning techniques) we used electric demand data from a period with a similar meteorology to 2020, in order to minimize the impact of heating or cooling systems, and attribute the differences to the effect of COVID-19 restrictions on electricity consumption. ...
Changes in primary emissions due to the COVID-19 lockdowns in Europe for the year 2020 have been estimated by considering fully open-access and near-real-time measured activity data from a wide range of information sources and with simple computational techniques. The estimates consist on a dataset of reduction factors that are both time- and country-dependent and provided for the following source categories: energy industry (power plants), manufacturing industry, road traffic, aviation, shipping and other stationary combustion activities such as residential and commercial-institutional activities. Inspired in other authors’ estimates for COVID reductions, the advantage of this methodology is that there is no use of machine learning, making this procedure more accessible to the general scientific community. We have followed a fast methodology that takes advantage of observed relationships between variables (e.g. temperature and energy demand) without needing special algorithms for finding those relationships. The comparison of our estimates with others from other authors indicate a reasonable agreement and pointing out that emissions dropped by a 17% on average in Europe, with large differences between sectors of activities and spatial heterogeneity. The most affected sector was aviation, with a spatial-averaged variation of −63% in emissions since the implementation of first restrictions with respect business-as-usual values. 2020 emission changes with respect to business-as-usual values in countries ranges from a −13% in Norway and Poland to a more than −20% in several Mediterranean countries as well as the United Kingdom. Two main periods of emission reductions have been identified.
... Artificial neural networks (ANNs) possess the ability to quicky adapt to variations of the given data, which offers an advantage considering that there are many factors that influence the variation of electricity demand throughout the day. Among them we can mention temperature changes [3]. For example, in summer, when the temperature rises, people will use cooling systems resulting in an increase in consumption. ...
Electric load forecasting is a central aspect of power generation planning as it allows the optimization of the production units. To date, no artificial neural network architectures (ANNs) were found that can precisely predict the consumption of energy at national level. In this paper, we propose the implementation of an artificial intelligence forecasting model that focuses on short term predictions. The current version is an enhancement of the previous approach which consisted of a full implementation in MATLAB. The algorithm was transposed in Python, using the new and updated tools such as TensorFlow and Keras, while taking into consideration a performance comparison between the two. To validate our model, we used, data from Romania between years 2008 to 2011. The implementation focuses on four main stages: restructuring and pre-processing the data, finding, and training the optimal model, refining the initial model, and retraining the neural network with new data. In terms of results, the current implementation decreased considerably the training time and returned a good prediction capability. On the other hand, the Python model was prone to overfitting, problem that was solved with techniques such as dropout and regularization layers. Regarding the architecture, it uses classical neurons as compared to other approaches in time series prediction that use LSTM cells. This simpler neural network offered higher efficiency in terms of computational resources while also being able to make accurate predictions.
... While their work is not directly related to retail sales, it emphasizes the importance of considering climate change in our analysis. In all the studies cited, authors account for climate change, such as [11] (not cited in the survey paper); as the data we have are over two years, we only need to account for climate. In addition, we consider the retail sales of items that are far less essential than electricity that is used for heating, cooling, cooking, and lighting. ...
... Our study also confirms this hypothesis that the change of heating modes will lead to the variation of the energy consumption structure. We also find that urban residential disposable income and heating degree days both have significant positive effects on natural gas consumption which is similar to the results of De Cian et al. [55]. In China, the power supply is still dominated by thermal power. ...
Under the background of global warming, the world is experiencing many extreme climate events. The temperature fluctuation associated with climate change is often considered as a primary driving force of electricity consumption. To better explore the impact of temperature on electricity consumption, this paper aims to analyze the question from the perspective of income growth, i.e., the moderating effect of income growth on the response of urban residential electricity to temperature changes. Using the unbalanced panel data of 23 cities in the Yangtze River Delta Urban Agglomeration from 2004 to 2015, we construct the response model of residential electricity consumption to income growth, heating degree days, cooling degree days and their interactions. The results show that heating demand, cooling demand and income growth significantly increase residential energy consumption. However, urban residential disposable income growth weakens the positive effect of heating demand and cooling demand on residential electricity consumption. Because, urban residents have obvious demand for improving indoor heating comfort and cooling comfort, the moderating effect of income is shown as the crowding-out effect. Residents may adjust the structure of heating energy consumption (from electricity to natural gas) and upgrade their electrical equipment to high energy efficiency equipment, thereby reducing residential electricity consumption. This study also indicates that urban residential energy consumption is very sensitive to changes in permanent populations. The main contribution of this study is proposing the hypothesis of the negative moderating effect of income growth on temperature changes and expanding the response model of residential electricity consumption to temperature changes. Finally, this paper also proposes the adaptive strategies and policy implications for climate change.
... Applications of panel studies involve micro-studies of household panels [7,8] and studies at the zip code [9,10], state [11], province [12] or national level [13,14]. Even though these panel data approaches mainly capture short-run instead of long-run responses to weather, Auffhammer and Mansur [6] conclude that they are the most promising method for estimating the effects of weather on energy consumption because of their capacity to deal with unobserved variables. ...
Fixed effects panel models are used to estimate how the electricity and gas consumption of various sectors and residents relate to temperature in Mexico, while controlling for the effects of income, manufacturing output per capita, electricity and gas prices and household size. We find non-linear relationships between energy consumption and temperature, which are heterogeneous per state. Electricity consumption increases with temperature, and this effect is stronger in warm states. Liquified petroleum gas consumption declines with temperature, and this effect is slightly stronger in cold states. Extrapolations of electricity and gas consumption under a high warming scenario reveal that electricity consumption by the end of the century for Mexico increases by 12%, while gas consumption declines with 10%, resulting in substantial net economic costs of 43 billion pesos per year. The increase in net energy consumption implies greater efforts to comply with the mitigation commitments of Mexico and requires a much faster energy transition and substantial improvements in energy efficiency. The results suggest that challenges posed by climate change also provide important opportunities for advancing social sustainability goals and the 2030 Agenda for Sustainable Development. This study is part of Mexico’s Sixth National Communication to the United Nations Framework Convention on Climate Change.
... However, Parkpoom and Harrison [3] used 11.7˚C (53˚F) to be the reference temperature in Thailand; Howden and Crimp [4] determined 17.5˚C (63.5˚F) to be the reference temperature for Sydney; Ahmed et al. [5] proposed 14 Global warming could lead to increases in CDDs and decreases in HDDs, concluded by Benestad [7], whose report indicates that climate change could trigger more energy consumption due to air conditioning in the hot areas. De Cian et al. [8] used the panel data from 31 countries to investigate the relationship between energy consumption and variations in temperature. Their empirical results suggest that higher average temperature leads to more energy consumption during hot seasons in the warmer countries, but less energy is consumed during cold seasons in the colder countries. ...
... The predictors of electricity demand are grouped into two: the building environment category and the household socio-economic characteristics category. The former category denoted by ( buildg_env) captures room size, cooling degree days (cool_deg) , heating degree days (heat_deg) , climatic condition of the geographical location of the housing unit (climate) and region of the housing unit (region) [see e.g., De Cian et al., 2007;Dolinar et al., 2010;Eskeland and Mideksa, 2010;Shaeffer et al., 2012;Taseska et al., 2012;Xu et al., 2012;Karimpour et al, 2015]. The second category involving socio-economic characteristics of households (denoted by hh_soc_eco) includes income, age, employment status (employment) , level of poverty (poverty) , level of education (education) , household size (hh_size) and gender [see e.g., Labanderia et al., 2006]. ...
In this paper, we estimate a demand model for electricity in the US residential sector using both the 2009 and 2015 RECS data. We find socio-economic characteristics and building patterns of households as the main drivers of residential electricity demand in the US. Also, controlling for regional and climatic effects is found to enhance the performance of the estimated models. Our results are further complemented with plausible scenario analyses and robustness checks.
... Once a household is active (individuals present), heating/cooling might be used; it depends primarily on current conditions and individuals' preference of "comfort zones" but also socio-economic factors. De Cian et al. [41] examined the interaction between income, temperature and energy demand, where an income interaction model was created, examining the income/temperature elasticity of electricity demand. Additionally, in Kane T. et al.'s [39] work (real world measurements), the rebound effect was greater than expected, which is attributed to the above socio-economic behaviour. ...
Demand Response (DR) is a Smart Grid technology aiming to provide demand regulation for dynamic pricing and ancillary services to the grid. Thermostatically controlled loads (TCLs) are among those with the highest potential for DR. Some of the challenges in modelling TCLs is the various factors that affect their duty cycle, mainly human behaviour and external conditions, as well as heterogeneity of TCLs (load parameters). These add an element of stochasticity, with detrimental impact on the aggregated level. Most models developed so far use Wiener processes to represent this behaviour, which in aggregated models, such as those based on Coupled Fokker-Planck Equations (CFPE), have a negligible effect as “white noise”. One of the main challenges is modelling the effect of external factors on the state of TCLs’ aggregated population and their impact in heterogeneity during operation. Here we show the importance of those factors as well as their detrimental effect in heterogeneity using cold loads as a case study. A bottom up detailed model has been developed starting from thermal modelling to include these factors, real world data was used as input for realistic results. Based on those we found that the duty cycle of some TCLs in the population can change significantly and thus the state of the TCLs’ population as a whole. Subsequently, the accuracy of aggregation models assuming relative homogeneity and based on small stochasticity (i.e. Wiener process with typical variance 0.01) is questionable. We anticipate similar realistic models to be used for real world applications and aggregation methods based on them, especially for cold loads and similar TCLs, where external factors and heterogeneity in time are significant. DR control frameworks for TCLs should also be designed with that behaviour in mind and the developed bottom up model can be used to evaluate their accuracy.
... Tym samym wpływ temperatury na popyt na energię elektryczną przyjmuje charakter nieliniowy. W celu lepszego zrozumienia tej zależności warto posłużyć się przedstawioną przez [2] klasyfikacją: kraje w zimnej i ciepłej strefie klimatycznej – zmienność goegraficzna; sezonowe wahania temperatury – zmienność sezonowa; rodzaj wykorzystywanych paliw – zmienność dostępności źródeł energii; poziom dochodu – zmienność poziomu dochodu. Dla krajów znajdujących się w ciepłej strefie klimatycznej oczekuje się, iż w lecie współczynnik korelacji pomiędzy temperaturą a zapotrzebowaniem na energię będzie dodatni. ...
Climate change/variability is one of the factors that affects the pattern of electrical energy consumption. Where the temperature is perceived as dominant and of crucial role in demand forecasting. Conducted analysis shows that energy demand pattern in Poland is slowly changing, and the impact of warmer days becomes more distinguishable.(Variation in the temperature impact on the power demand in Poland over the years 2002-2015).
... Yet, temperature-energy demand relations are used to project, seasonally and over the long term, the capacity requirements of the energy sector, fuel use in electricity generation, space heating and cooling needs by final consumers, and associated emissions (e.g. De Cian et al., 2007;Kirshen et al., 2008;Mansur et al., 2005;Moral-Carcedo and Vicéns-Otero, 2005). ...
... In Serbia and Montenegro low income people heats only a small part of the houses to decrease the energy cost. A very recent study [34] reports that in some Eastern European countries like Bulgaria, Romania, Lithuania, Poland and Latvia the percentage of the households unable to pay to keep their dwelling adequately warm ranges between 20 and 32% with the higher values found in Bulgaria and Romania. The above value is much higher than the average percentage for the European Union countries which is close to 12%. ...
Urban heat island and global warming increase significantly the ambient temperature. Higher temperatures have a serious impact on the electricity consumption of the building sector increasing considerably the peak and the total electricity demand. The present paper aims to collect, analyze and present in a comparative way existing studies investigating the impact of ambient temperature increase on electricity consumption. Analysis of eleven studies dealing with the impact of the ambient temperature on the peak electricity demand showed that for each degree of temperature increase, the increase of the peak electricity load varies between 0.45% and 4.6%. This corresponds to an additional electricity penalty of about 21 (±10.4) W per degree of temperature increase and per person. In parallel, analysis of fifteen studies examining the impact of ambient temperature on the total electricity consumption, showed that the actual increase of the electricity demand per degree of temperature increase varies between 0.5% and 8.5%.
... Because of the non-linearity of many effects , this may have large implications at higher temperature increases. All the parameters are shown inTable A5 in Appendix A. Finally, the effects on the demand for energy are based on de Cian et al. (2007). The estimates for cold countries are used in regions with average annual temperature below + 15 °C and for hot countries in regions with annual temperature above. ...
Economic evaluations of solar radiation management (SRM) usually assume that the temperature will be stabilized, with no economic impacts of climate change, but with possible side-effects. We know from experiments with climate models, however, that unlike emission control the spatial and temporal distributions of temperature, precipitation and wind conditions will change. Hence, SRM may have economic consequences under a stabilization of global mean temperature even if side-effects other than those related to the climatic responses are disregarded. This paper addresses the economic impacts of implementing two SRM technologies; stratospheric sulfur injection and marine cloud brightening. By the use of a computable general equilibrium model, we estimate the economic impacts of climatic responses based on the results from two earth system models, MPI-ESM and NorESM. We find that under a moderately increasing greenhouse-gas concentration path, RCP4.5, the economic benefits of implementing climate engineering are small, and may become negative. Global GDP increases in three of the four experiments and all experiments include regions where the benefits from climate engineering are negative.
Copyright © 2015 Elsevier B.V. All rights reserved.
... In these three cases, variables exogenous to the model are involved and their modification is straightforward. Changes in tourists expenditure are modelled as changes in demand addressing the " market services sector " which includes recreational services (Bigano et al., 2005); changes in health care expenditures are translated into changes in the public and private demand for the " non market services " sector which includes health services (Bosello et al., 2006); changes in regional demand for oil, gas and electricity are modelled as changes in the demand for the output of respective industries (De Cian et al. 2007). In these last cases, variables which are endogenous to the model are concerned. ...
Deforestation is one of the major sources of greenhouse gases (GHG) emissions, accounting for around 17% of total global GHG discharges. As the role forests play in the global carbon cycle has been widely recognized, several studies analysing the potential contribution of avoided deforestation credits in a carbon market have already been performed, showing that these credits should play a substantial role in an overall portfolio of mitigation strategies. Using a dynamic, multiregional Computable General Equilibrium (CGE) model, the ICES model (Intertemporal Computable Equilibrium System), this paper follows a novel approach, since deforestation emission reductions are not linked to a global carbon market, as commonly used. Instead, we use a global warming approach which provides an additional valuation criteria: the market general equilibrium value of halting/reducing deforestation, based on climate change impacts. This exercise consists on the formulation of a scenario where carbon emissions from deforestation are reduced by 50% and 100%, thereby producing a different CO2 concentration levels in the atmosphere and a corresponding variation in temperature. Those changes in temperature in turn impact the economy at various levels, and the corresponding indirect effects are then assessed. Moreover, such an exercise may provide additional information to policy makers considering the creation/participation of an international fund to protect tropical forests. In fact, market valuation for avoided deforestation can be provided not only for a complete halt of deforestation but also for different reduction targets. Finally, and taking advantage of the dynamic feature of the model, different paths for a same global level of reduced deforestation are simulated, shedding new lights on the temporal value of reduced deforestation.
... Amongst others, Wilbanks et al. (2007b) for the USA, Aebischer et al. (2007) for Switzerland and Europe, Dolinar et al. (2010) for Slovenia, Wang et al. (2010) for Australia, Ward (2008 for Yorkshire in the UK and Ferrand and Singh (2010) for India. Some of these studies assess the determinants of heating and cooling demands based on multi-country panel studies (Cian et al, 2007; Petrick et al, 2010). Very few studies analyze the impacts at global level. ...
The energy sector is not only a major contributor to greenhouse gases, it is also vulnerable to climate change and will have to adapt to future climate conditions. The objective of this study is to analyze the impacts of changes in future temperatures on the heating and cooling services of buildings and the resulting energy and macro-economic effects at global and regional levels. For this purpose, the techno-economic TIAM-WORLD (TIMES Inte- grated Assessment Model) and the general equilibrium GEMINI-E3 (General Equilibrium Model of International-National Interactions between Economy, Energy and Environment) models are coupled with a climate model, PLASIM-ENTS (Planet-Simulator - Efficient Numerical Terrestrial Scheme). The key results are as follows. At the global level, the cli- mate feedback induced by adaptation of the energy system to heating and cooling is found to be insignificant, partly because heating and cooling-induced changes compensate and partly because they represent a limited share of total final energy consumption. However, significant changes are observed at regional levels, more particularly in terms of addi- tional power capacity required to satisfy additional cooling services, resulting in increases in electricity prices. In terms of macro-economic impacts, welfare gains and losses are associated more with changes in energy exports and imports than with changes in energy consumption for heating and cooling. The rebound effect appears to be non-negligible. To conclude, the coupling of models of different nature was successful and showed that the energy and economic impacts of climate change on heating and cooling remain small at the global level, but changes in energy needs will be visible at more local scale.
... Unfortunately, application of panel-data modeling in the field of water resources management has so far been limited, although it has been widely applied in economics research (e.g. Arbués et al. 2004; De Cian et al. 2007; Moeltner and Stoddard 2004; Zhang and Fan 2001). The objective of this study was to investigate the capabilities and potential of panel-data modeling as a tool for the prediction of groundwater-level fluctuations in the Neishaboor plain, Iran. ...
The aim of this research was to predict groundwater levels in the Neishaboor plain, Iran, using a “panel-data” model. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time span. Firstly, the available observation wells in the Neishaboor plain were clustered according to their fluctuation behavior using the “Ward” method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation and temperature, as independent variables, were estimated by the inverse-distance method. Finally, the performance of different panel-data regression models such as fixed-effects and random-effects models were investigated. The results showed that the two-way fixed-effects model was superior. The performance indicators for this model (R
2 = 0.97, RMSE = 0.05 m and ME = 0.81 m) reveal the effectiveness of the method. In addition, the results were compared with the results of an artificial-neural-network (ANN) model, which demonstrated the superiority of the panel-data model over the ANN model.
The authors examined the possible adverse effect of hot temperature on firms’ profitability and stock performance, using measures of various scorching temperature variables as exogenous indicators of firms’ weather risk. The results show that scorching temperatures led to declines in the sample firms’ earnings caused by changes in sales, expenses, and productivity. The more extreme the hot weather, the more the earnings declined. In further investigations, the authors found that this impact of scorching temperatures was heterogeneous over time and across sectors, geographical locations, and levels of economic development. The impact was most severe in low-latitude regions, especially tropical and subtropical countries from 2013 onward. It was also found that extremely hot temperatures negatively influenced the stock returns of individual firms. This effect, mediated by the firm’s earnings, was especially strong for value stocks and small cap stocks. Our results also show that the firms’ profitability and stock performance are exacerbated by the increases in global average temperatures and provide the direct evidences of the adverse impact of global warming on individual firms. Finally, weather uncertainty aggravated the volatility of earnings and stock returns.
European policy makers are increasingly interested in higher spatial representations of future macro-economic consequences from climate-induced shifts in the energy demand. Indeed, EU sub-national level analyses are currently missing in the literature. In this paper, we conduct a macro-economic assessment of the climate change impacts on energy demand at the EU sub-national level by considering twelve types of energy demand impacts, which refer to three carriers (petroleum, gas, and electricity) and four sectors (agriculture, industry, services, and residential). These impacts have been estimated using climatic data at a high spatial resolution across nine Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP) combinations. The impacts feed into a Computable General Equilibrium model, whose regional coverage has been extended to the sub-national NUTS2 and NUTS1 level. Results show that negative macroeconomic effects are not negligible in regions located in Southern Europe mainly driven by increased energy demand for cooling. By 2070, we find negative effects larger than 1% of GDP, especially in SSP5-RCP8.5 and SSP3-RCP4.5 with a maximum of − 7.5% in Cyprus. Regarding regional differences, we identify economic patterns of winners and losers between Northern and Southern Europe. Contrasting scenario combinations, we find that mitigation reduces adverse macro-economic effects for Europe up to a factor of ten in 2070, from 0.4% GDP loss in SSP5-RCP8.5 to 0.04% in SSP2-RCP2.6.
This study contributes to the literature on whether financial development stimulates technical energy efficiency (TEE) or not, by addressing core biases that creep into the relationship and thereby reducing the ability to draw causal inferences from financial development to TEE. Our approach is based on the instrumental stochastic frontier technique, where biases in the frontier and inefficiency equations are dealt with using external instrumental variables. The legal system origin of the country and life expectancy at birth were used as instruments for financial development and income, respectively. The current study demonstrates substantial bias in income elasticity, the estimate of energy efficiency, and the effect of financial development on energy efficiency. Both income elasticity and energy efficiency estimate risk upward bias. Equally, the effect of financial system development and financial institution development on TEE risk downward bias. Other results show that all aspects of financial institution development stimulate TEE, but access to financial institutions is more important. These results raise caution about future studies’ estimates of the effect of financial development on TEE. Though this study has demonstrated the potency of external instruments in dealing with the bias in the coefficient estimates, we consider this might prove to be a luxury solution in some cases due to data limitation, context differences, and theory. In those circumstances, reliance on internal instruments might prove to be the second-best option.
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.
Urban heat islands (UHIs) and their energy consumption are topics of widespread concern. This study used remote sensing images and building and meteorological data as parameters, with reference to Oke's local climate zone (LCZ), to divide urban areas according to the height and density of buildings and land cover types. While analyzing the heat island intensity, the neural network training method was used to obtain temperature data with good temporal as well as spatial resolution. Combining degree-days with the division of LCZs, a more accurate distribution of energy demand can be obtained by different regions. Here, the spatial distribution of buildings in Shenyang, China, and the law of land surface temperature (LST) and energy consumption of different LCZ types, which are related to building height and density, were obtained. The LST and energy consumption were found to be correlated. The highest heat island intensity, i.e., UHILCZ 4, was 8.17°C. The correlation coefficients of LST with building height and density were −0.16 and 0.24, respectively. The correlation between urban cooling energy demand and building height was −0.17, and the correlation between urban cooling energy demand and building density was 0.17. The results indicate that low- and medium-rise buildings consume more cooling energy.
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 impacts of climate change on industrial water demand (IWD) directly affect IWD management. In this study, we propose a framework for evaluating different impacts of climate change on IWD by sector, considering both direct and indirect effects. Data from 34 industrial sectors in Hebei Province, China, showed that the impacts of climate change varied by sector, and IWD in 22 of the 34 sectors was affected, ranging from -15.11 to 37.36% under the average rates of change in precipitation and temperature. The corresponding volumetric change of IWD was between -31.148 and 141.890 million m ³ , considering the difference in the water demand scale between sectors. The overall impact of climate change on IWD gradually decreased from more than 12.8% to approximately 4.1% from 2007 to 2016 due to the substantial improvement of water use efficiency. The indirect effects caused by the total industrial output value offset about 60-50% of the direct growth impacts. By contrast, the increase in IWD caused by the rise in temperature accounted for nearly 90% of the change, whereas only approximately 10% was caused by the decrease in precipitation. In general, an industrial sector may be directly and indirectly affected by temperature and precipitation, and the different impacts may offset each other. This study provides evidence and explanations for the heterogeneity of climate change impacts, and the research results can provide information for regional industrial water resource managers to adapt to climate change.
The description of the relationship between temperature (T) and electricity consumption (EC) is key to improving our understanding of a potential climate change amplification feedback and, thus, energy planning. We sought to characterize the relationship between the EC and daily T of different regions of Argentina and use these historical relationships to estimate expected EC under T future scenarios. We used a time series approach to model EC, removing trends and seasonality and accounting for breaks and discontinuities. EC and T data were obtained from Argentine Wholesale Market Administrator Company and global databases, respectively. We evaluate the T-EC model for the period between 1997 and 2014 and two sub-periods: 1997–2001 and 2011–2014. We use modeled temperature projections for the 2027–2044 period based on the Representative Pathway Concentration 4.5 together with our region-specific T-EC models to predict changes in EC due to T changes. The shape of the T-EC relationships is quite stable between periods and regions but varies according to the temperature gradient. We find large increases in EC in warm days (from 40 to 126 Wh/cap/°C) and a region-specific response to cold days (from flat to steep responses). The T at which EC was at minimum varies between 14 and 20 °C and increase in time as mean daily T also increase. Estimated temperature projections translate into an average increase factor of 7.2 in EC with contrasting relative importance between regions of Argentina. Results highlight potential sensitivity of EC to T in the developing countries.
Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and temperature. Due to the tropical climate of Thailand, the marginal impact of weather variables on electricity demand is worth analyzing. Therefore, this paper primarily focuses on the impact of temperature and other deterministic variables on Thai electricity demand. Accuracy improvement is also considered during model design. Based on the characteristics of demand, the overall dataset is divided into four different subgroups and models are developed for each subgroup. The regression models are estimated using Ordinary Least Square (OLS) methods for uncorrelated errors, and General Least Square (GLS) methods for correlated errors, respectively. While Feed Forward Artificial Neural Network (FF-ANN) as a simple Deep Neural Network (DNN) is estimated to compare the accuracy with regression methods, several experiments conducted for determination of training length, selection of variables, and the number of neurons show some major findings. The first finding is that regression methods can have better forecasting accuracy than FF-ANN for Thailand’s dataset. Unlike much existing literature, the temperature effect on Thai electricity demand is very interesting because of their linear relationship. The marginal impacts of temperature on electricity demand are also maximal at night hours. The maximum impact of temperature during night hours happens at 11 p.m., is 300 MW/ ° C, about 4 % rise in demand while during day hours, the temperature impact is only 10 MW/ ° C to 200 MW/ ° C about 1.4 % to 2.6 % rise.
Steep increases in air temperatures and CO2 emissions have been associated with the global demand for energy. This is coupled with population growth and improved living standards that encourages the reliance on mechanical acclimatization. Lighting energy alone is responsible for a large portion of total energy consumption in office buildings; and the demand for artificial light is expected to grow in the next years. One of sustainable approaches to enhance energy-efficiency is to incorporate daylighting strategies, which entail the controlled use of daylight inside buildings. Daylight simulation is an active area of research that offers accurate estimations, yet requires a complex set of inputs. Even with today’s computers, simulations are computationally expensive and time-consuming, hindering to acquire accelerated preliminary approximations in acceptable timeframes, especially for the iterative design alternatives. Alternatively, predictive models that build on machine learning algorithms have granted much interest from the building design community due to their ability to handle such complex non-linear problems, acting as proxies to heavy simulations. This research presents a review on the growing directions that exploit machine learning to rapidly predict daylighting performance inside buildings, putting a particular focus on scopes of prediction, used algorithms, data sources and sizes, besides evaluation metrics. This work should improve architects’ decision-making and increase the applicability to predict daylighting. Another implication is to point towards knowledge gaps and missing opportunities in the related research domain, revealing future trends that allow for such innovative approaches to be exploited more commonly in Architectural practice.
This paper combines available assessments on the impacts of climate change with climate projections from an earth system model to estimate the economic consequences for 15 sectors in 11 world regions. If the standard way of thinking about how economies work, the theory of market equilibrium, applies. The aim is to identify potential sources of conflicts between economic interests if the world succeeds in reducing emissions from a pathway with high emissions, where temperatures increase at around +6 °C in 2100 to a lower emission path, where the temperatures increase by +3 °C. Both pathways are recommended by the Intergovernmental Panel on Climate Change as standard descriptions of anthropogenic drivers for climate modelling. It is difficult to identify conflicts between developed and developing regions, but conflicts between sector interests are apparent. Fossil fuel extracting industries lose by mitigation in all regions. Nearly all other sectors gain in all regions. The results also reveal potential conflicts between socioeconomic groups. The main challenges relate to conflicts of interest between generations. The present generation can commence on a transformation to low-carbon economies without notable economic consequences, but the next generation stands to lose, while generations beyond will clearly gain.
Price elasticities of energy demand have become increasingly relevant in estimating the socio-economic and environmental effects of energy policies or other events that influence the price of energy goods. Since the 1970s, a large number of academic papers have provided both short and long-term price elasticity estimates for different countries using several models, data and estimation techniques. Yet the literature offers a rather wide range of estimates for the price elasticities of demand for energy. This paper quantitatively summarizes the recent, but sizeable, empirical evidence to facilitate a sounder economic assessment of (in some cases policyrelated) energy price changes. It uses meta-analysis to identify the main factors affecting short and long term elasticity results for energy, in general, as well as for specific products, i.e., electricity, natural gas, gasoline, diesel and heating oil.
ResumenDebido a la unificación tarifaria que se presentó en el Departamento de Antioquia en el primer semestre del año 2008 ocasionada por la adquisición del 100% de la participación accionaria de EADE por parte de EPM, se originaron una serie de implicaciones de bienestar en los usuarios del servicio de electricidad del Departamento. Específicamente se encontró que dicho cambio implicó una variación en el bienestar del agente representativo promedio del Departamento equivalente al -0,53%, en donde los efectos a nivel de estrato y ubicación geográfica son bastante heterogéneos; en particular los hogares de ingresos bajos ubicados fuera del área metropolitana tienen una mejora en su bienestar cercana al 1,5% como porcentaje de sus ingresos, mientras que los estratos medios y altos pertenecientes al Valle de Aburrá evidencian pérdidas de bienestar del orden de 1,5%. Vale la pena destacar que en términos generales las elasticidades precio por estrato son crecientes con éste, en tanto que las elasticidades ingreso de la demanda son decrecientes con el mismo. Igualmente se encontró que las elasticidades de largo plazo son superiores a las evidenciadas en el corto plazo, mientras que el consumo de electricidad presenta un efecto estacional en el cuarto trimestre del año, y que los bienes sustitutos no desempeñan un rol estadísticamente significativo en la demanda del servicio una vez tomada la decisión de inversión en éstos. Finalmente, un ejercicio de simulación alternativo, el cual implicaría cambios regulatorios de orden nacional, pone de presente que se pueden alcanzar ganancias de bienestar, tanto a nivel de distribución del ingreso como en términos agregados, sin ir en detrimento de la posición financiera de la empresa unificada. AbstractIn the Department of Antioquia a change was evidenced in the welfare level of its citizens because the unification of the price service of electricity due to EPM acquired the 100% of the EADE’s shares. Specifically, the new price service implied a decline in the welfare of 0.53% at aggregated level. However, the changes in the welfare level are too heterogeneous when the data is seen by income level and location. For example, households with lower income and outside the metropolitan area increased their welfare level 1.5%, whereas households in the metropolitan area with higher income experienced a loss of 1.5% in their welfare level. It is important to realize that demand’s price elasticities are increasing with stratum, while the demand’s income elasticities are decreasing. Additionally, the short term elasticities are smaller than long term, the demand for electricity has a stationary effect in the fourth quarter and there is no effect due to substitute services. Finally, a simulated scenario that implies a change in the national legal field shows that it is possible to obtain better distributional effects and higher increments at aggregated level in the welfare without a detriment in the financial position of the firm.
The paper presents a model-based approach describing the impacts of climate change on the European energy system. Existing analyses only estimate a limited range of climate impacts over a limited geographical area. Using the POLES model and the results from several climate models, the present paper quantifies the main impacts of climate change on the European energy sector, country by country, thus achieving progress in this direction. As far as energy demand is concerned, our main finding is that higher temperatures will mean that air-conditioning will consume more energy, reaching about 53 Mtoe by 2100 in a scenario with no strong emissions constraints (A1B). On the other hand, less energy will be consumed for heating buildings, falling by about 65 Mtoe per year. This represents a net decrease in energy consumption of about 12 Mtoe by 2100. On the supply side, more constrained and expensive operating conditions for electric power plants will result in lower electricity generation by thermal, nuclear and hydro-power plants, with a maximum decrease of about 200 TWh in 2070 in the A1B scenario and 150 TWh in 2060 and 2080 for a low emissions scenario (E1). These effects vary a great deal across Europe and remain very dependent on the uncertainties affecting the results of the various climate models. This overall uncertainty may inhibit effective decisions. However, the study offers insights not otherwise available without the full coverage of the energy system provided by POLES and climate features provided by climate models. The study identifies the main impacts of climate change in a strategic sector and provides an “order of magnitude” or “central trend” for these impacts, which might be useful in an adaptive policy of act, learn and then act again.
This chapter investigates the impact of climate change on the electricity sector. We quantified two main impact chains: (1) impact of climate change on electricity supply, in particular on hydropower and (2) impact of climate change on electricity demand, in particular for heating and cooling. The combined effects of these two impact chains were investigated using the optimization model HiREPS. This takes the hourly resolution of the electricity system into account and considers, in particular, the interaction of the Austrian and German electricity markets. The results show that by 2050 there is a robust shift in the generation of hydroelectric power from summer to winter periods and a slight overall reduction in hydropower generation. The absolute increase in electricity demand is moderate. However, the electricity peak for cooling approximately reaches the level of the overall electricity load in 2010. These two effects—decreasing hydropower supply and increasing cooling electricity peak load (cf. Chap. 13)—lead to moderate sectoral climate change costs in 2050 compared to the baseline scenario without climate change. Regarding macroeconomic effects coming from climate change impacts on the electricity sector we see negative impacts on welfare as well as GDP. However, significant uncertainties remain and the effect of extreme events and natural hazards on electricity supply and transmission infrastructure also needs further examination. The costs of a potential increase in black out risk may be orders of magnitude higher than the costs indicated in our mid-range scenario.
Spanis Abstract: Las reformas de mediados de los noventa, basadas en las leyes 142 y 143 de 1994, introdujeron cambios institucionales y metodológicos en la regulación tarifaria para los servicios de electricidad y acueducto en Colombia. Este artículo, además de reseñar dichos cambios, evalúa a través de un contrafactual cómo hubiese sido la evolución tarifaria si no se hubiese presentado la reforma de los 90’s. Así mismo, estima las elasticidades precio y gasto para dichos servicios mediante el Sistema Casi Ideal de Demanda (AIDS) y con el cálculo de la variación equivalente establece si la regulación generó una mejora en el bienestar de los consumidores.English Abstract: Mid nineties reforms, based on laws 142 and 143 from 1994, introduced institutional and methodological changes in tariff regulation for electricity and aqueduct services in Colombia. This paper, besides indentifying such changes, evaluates trough a contrafactual how would have been the evolution of the tariffs if the reform had not been in the 90's. Also, the paper estimates price elasticities and expenditure for the mentioned services from an Almost Ideal Demand System and whit the calculation of the equivalent deviation it establishes if the regulation caused an increase in consumers' wealth.
Climate observations in recent years indicate that the effects of climate change events are apparently having an increasing impact on society. These impacts will likely also affect the building sector. Numerous studies have been conducted to assess future building energy consumption rates. However, these studies often do not take into account climatic variability and consumer reactions towards a temperature shift. A literature review on climate change impacts for commercial buildings and their technical services in the tropics was carried out. This review focuses on the buildings’ contributions towards climate change as well as climate change impacts on building structures, changing patterns of energy use and peak demands, building heating and cooling requirements, thermal comfort and emissions impacts. In general, buildings in regions with a predicted increase in temperature will need more cooling and less heating loads. Thus, building energy consumption and carbon emissions are projected to rise during its operational phase. In addition, the erratic weather trends will also affect the building efficiency and sustainability, indoor air quality and thermal comfort. Even though the existing literature on this issue has increased substantially in recent years, there is still a need for further research in tropical climates as the climate change impacts vary with the different seasons, periods and regions.
This paper studies the impact of weather variation on energy use by using five-minute interval weather-energy data obtained from two residential houses: house 1 is a conventional house with advanced efficiency features and house 2 is a net-zero solar house with relatively more advanced efficiency features. Our result suggests that energy consumption in house 2 is not as sensitive to changes in weather variables as the conventional house. On average, we find that a one unit increase in heating and cooling degree minutes increases energy use by about 9% and 5% respectively for house 1 and 5% and 4% respectively for house 2. In addition, our findings suggest that non-temperature variables such as solar radiation and humidity affect energy use where the sensitivity rates for house 2 are consistently lower than that of house 1. Furthermore our result suggests that the sensitivity of energy use to weather depends on the season and specific time of the day/night.
This paper presents the analysis of the impact of weather conditions on the maximum electricity demand in Qatar during the whole year 2012. It points out the maximum daily air temperature as the most influential climate (meteorological) parameter. Correlation between maximum daily temperature and maximum daily electricity demand are also indicated and analyzed. It is noticed that there is a linear correlation between these two variables for maximum temperature values above 22°C because of the air-conditioning type of the major load. During extremely hot summer periods, there is a tendency of increased electricity consumption because of air conditioning. A timely and accurate weather forecast can certainly help prevent the electrical power system overload and reduce the risk of possible power system damage.
This paper evaluates the impacts of climate change to European economies under an increase in global mean temperature at +2 °C and +4 °C. It is based on a summary of conclusions from available studies of how climate change may affect various sectors of the economies in different countries. We apply a macroeconomic general equilibrium model, which integrates impacts of climate change on different activities of the economies. Agents adapt by responding to the changes in market conditions following the climatic changes, thus bringing consistency between economic behaviour and adaptation to climate change. Europe is divided into 85 sub-regions in order to capture climate variability and variations in vulnerabilities within countries. We find that the impacts in the +2 °C are moderate throughout Europe, with positive impacts on GDP in some sub-regions and negative impacts down to 0.1 per cent per year in others. At +4 °C, GDP is negatively affected throughout Europe, and most substantially in the southern parts, where it falls by up to 0.7 per cent per year in some sub-regions. We also find that climate change causes differentiations in wages across Europe, which may cause migration from southern parts of Europe to northern parts, especially to the Nordic countries.
Since 2005, the European Union Emissions Trading Scheme (EU ETS) has seen a rapid growth in trading volume activity, with 1.44 billion tons of CO2 traded in 2007. The total value of these trading transactions was €24.1 billion in 2007, confirming the EU ETS as the largest emissions trading system by transaction value. In this paper, we test whether this market exhibits predictability of prices in terms of momentum (i.e., positive/negative changes continuing) and overreaction (i.e., positive/negative changes reversing). We test whether momentum and overreaction exist in the carbon price, and if they do, whether they result in profitable trading strategies. We document a robust short-term momentum and medium-term overreaction within the EU ETS. We also find statistically significant returns in a number of strategies tested. The strategies employed provide excess returns that remain achievable in a practical sense even after transaction costs have been taken into consideration. Our results therefore provide evidence that the EU ETS is not informationally efficient.
This paper estimates a multinomial discrete-continuous fuel choice model of both households and firms in order to determine the sensitivity of national energy demand to climate change. We find that consumers switch from natural gas, oil, and other fuels to electricity as climate warms and that overall energy demand - especially electricity demand - increases. The model implies that warming will increase American energy expenditures, resulting in welfare damages that increase as temperatures rise. Increases in electricity expenditures for cooling are partially offset by reductions in expenditures on other fuels for heating. Given a five degree Celsius increase in temperature by 2100, we predict an annual welfare loss of $40 billion, borne primarily by residential customers.
This paper studies the economic implications of climate-change-induced variations in tourism demand, using a world CGE model. The model is first re-calibrated at some future years, obtaining hypothetical benchmark equilibria, which are subsequently perturbed by shocks, simulating the effects of climate change. We portray the impact of climate change on tourism by means of two sets of shocks, occurring simultaneously. The first set of shocks translate predicted variations in tourist flows into changes of consumption preferences for domestically produced goods. The second set reallocate income across world regions, simulating the effect of higher or lower tourists’ expenditure. Our analysis highlights that variations in tourist flows will affect regional economies in a way that is directly related to the sign and magnitude of flow variations. At a global scale, climate change will ultimately lead to a welfare loss, unevenly spread across regions.
The responsiveness of energy demand to pricing is shown to be dependent on temperature and vice versa. This is investigated empirically using residential electricity demand data obtained under conditions of price variation from a British time-of-use pricing experiment. Results confirm that consumer response to higher electricity prices may be conditional on temperature levels, particularly during the daytime and for households with high overall levels of electricity consumption.
We use a general equilibrium model of the world economy, and a regional economic growth model, to assess the economic implications of vulnerability from extreme meteorological events, induced by the climate change. In particular, we first consider the impact of climate change on ENSO and NAO oceanic oscillations and, subsequently, the implied variation on regional expected damages. We found that expected damages from extreme events are increasing in the United States, Europe and Russia, and decreasing in energy exporting countries. Two economic implications are taken into account: (1) short-term impacts, due to changes in the demand structure, generated by higher/lower precautionary saving, and (2) variations in regional economic growth paths. We found that indirect stort-term effects(variations in savings due to higher or lower likelihood of natural disasters) can have an impact on regional economies, whose order of magnitude is comparable to the one of direct damages. On the other hand, we highlight that higher vulnerability from extreme events translates into higher volatility in the economic growth path, and vice versa.
The literature on social capital has strongly increased in the last two decades, but there still is a lack of substantial empirical evidence about the determinants of international trust. This empirical study analyses a cross-section of individuals, using micro-data from the World Values Survey, covering 38 countries, to investigate trust in international organizations, specifically in the United Nations. In line with previous studies on international trust we find that political trust matters. We also find that social trust is relevant, but contrary to previous studies the results are less robust. Moreover, the paper goes beyond previous studies investigating also the impact of geographic identification, corruption and globalization. We find that a higher level of (perceived) corruption reduces the trust in the UN in developed countries, but increases trust in developing and transition countries. A stronger identification with the world as a whole also leads to a higher trust in the UN and a stronger capacity to act globally in economic and political environment increases trust in the UN.
The economy-wide implications of sea level rise in 2050 are estimated using a static computable general equilibrium model. This allows for a better estimate of the welfare effects of sea level rise than the common direct cost estimates; and for an estimate of the impact of sea level rise on greenhouse gas emissions. Overall, general equilibrium effects increase the welfare costs of sea level rise, but not necessarily in every sector or region. In the absence of coastal protection, economies that rely most on agriculture are hit hardest. Although energy is substituted for land, overall energy consumption falls with the shrinking economy, hurting energy exporters. With full coastal protection, GDP increases, particularly in regions with substantial dike building, but utility falls, least in regions that protect their coasts and export energy. Energy prices rise and energy consumption falls. The costs of full protection exceed the costs of losing land. The results also show direct costs – the usual method for estimating welfare changes due to sea level rise – are a bad approximation of the general equilibrium welfare effects; previous estimates of the economic impact of sea level rise are therefore biased. Copyright Springer Science+Business Media, Inc. 2007
In this paper, we consider two basic aspects of demand analysis, with application to the demand for natural gas in the residential and commercial market. The more fundamental one consists in the formulation of a demand function for commodities--such as natural gas--whose consumption is technologically related to the stock of appliances. We believe that in such markets, the behavior of the consumer can be described best in terms of a dynamic mechanism. Related to this is the more specific problem of estimating the parameters of the demand function, when the demand model is cast in dynamic terms and when observations are drawn from a time series of cross sections. Accordingly, this paper is centered around these two major themes, although, as the title suggests, the emphasis is placed on the second one. In Section 1, we present the theoretical formulation of the dynamic model for gas. In Section 2, the results of the estimation of the gas model by ordinary least squares methods are presented. These results, together with more fundamental theoretical considerations, suggest a different approach. The essence of this approach, which is not restricted to the gas model, is discussed in Section 3, while two alternative procedures for estimating the coefficients of the dynamic model in the light of this new approach are proposed in Section 4. It is subsequently shown that the application of these procedures to the gas data produces results that are reasonable on the basis of a priori theoretical considerations.
This article discusses the problem of obtaining short-run and long-run elasticities of energy demand for each of 49 states in the United States using data for 21 years. Estimation using the time series data by each state gave several wrong signs for the coefficients. Estimation using pooled data was not valid because the hypothesis of homogeneity of the coefficients was rejected. Shrinkage estimators gave more reasonable results. The article presents in a unified framework the classical, empirical Bayes, and Bayes approaches for deriving these estimators.
This study uses a three-step approach to estimate the impact of global warming on U.S. energy expenditures for space heating and cooling in residential and commercial buildings. First, average results from six different global circulation models are used to estimate the change in heating and cooling degree days in five U.S. climate zones associated with a 1{degree} centigrade (C) global warming. Second, the change in degree days is mapped into a corresponding change in U.S. energy use for space conditioning, taking account of differences in population and baseline space conditioning intensity levels across regions, under the assumption that desired indoor temperature is unaffected by climate change. Finally, we estimate the associated change in energy expenditures. We find that a global warming of PC would reduce projected U.S. energy expenditures in 2010 by $5.5 billion (1991 dollars). This contrasts with earlier studies which have suggested modest global warming would increase U.S. expenditures on space conditioning energy. 21 refs., 2 tabs.
The appendix contains health research studies for The Potential Effects of Global Climate Change on the United States report for Congress (1989).
Greenhouse gas emissions related to fossil fuel use is one of the principle causes of the enhanced greenhouse effect and hence future climate change. The threats posed by global warming continue to dominate environmental concerns and have led to international accord and to national policy on the issues. While various means of curbing emissions have been considered, most credence is given to some price-based control. It is therefore vital to consider the responsiveness of energy markets to economic controls in order to assess the feasibility of emissions reduction. This volume deals with various aspects of economic modelling of energy elasticities in six chapters. The second part of seven chapters considers wider issues of technological change and price and demand adjustments. -N.Adger
A simulation model of international tourist flows is used to estimate the impact of a carbon tax on aviation fuel. The effect of the tax on travel behaviour is small: A global $1000/tC would change travel behaviour to reduce carbon dioxide emissions from international aviation by 0.8%. This is because the imposed tax is probably small relative to the air fare. A $1000/tC tax would less than double air fares, and have a smaller impact on the total cost of the holiday. In addition, the price elasticity is low. A carbon tax on aviation fuel would particularly affect long-haul flights, because of high emissions, and short-haul flights, because of the emission during take-off and landing. Medium distance flights would be affected least. This implies that tourist destinations that rely heavily on short-haul flights (that is, islands near continents, such as Ireland) or on intercontinental flights (e.g., Africa) will see a decline in international tourism numbers, while other destinations may see international arrivals rise. If the tax is only applied to the European Union, EU tourists would stay closer to home so that EU tourism would grow at the expense of other destinations. Sensitivity analyses reveal that the qualitative insights are robust. A carbon tax on aviation fuel would have little effect on international tourism, and little effect on emissions.
This paper presents a statistical analysis of time series regression models for longitudinal data with and without lagged dependent variables under a variety of assumptions about the initial conditions of the processes being analyzed. The analysis demonstrates how the asymptotic properties of estimators of longitudinal models are critically dependent on the manner in which samples become large: by expanding the number of observations per person, holding the number of people fixed, or by expanding the number of persons, holding the number of observations per person fixed. The paper demonstrates which parameters can and cannot be identified from data produced by different sampling plans.
The demand for electricity is a key variable because its links to economic activity and development; however, the electricity consumption also depends on other non-economic variables, notably the weather. The aim of this study is to analyse the effect of temperatures on the variability of the Spanish daily electricity demand, and especially to characterise the non-linearity of the response of demand to variations in temperature. In this article, we explore the ability of Smooth Transition (STR), Threshold Regression (TR), and Switching Regressions (SR) models, to handle both aspects. As we conclude, the use of LSTR approach offers two main advantages. First, it captures adequately the smooth response of electricity demand to temperature variations in intermediate ranges of temperatures. Second, it provides a method to analyse the validity of temperature thresholds used to build the “cooling degree days” (CDD) and “heating degree days” (HDD) variables traditionally employed in the literature.
Some new evidence on the income own-price elasticities of demand for energy consumption by consumers is presented. Estimates are derived from a complete system of cross-country demand equations with energy being one of the commodities considered.
Quarterly data for Israel are used to compare and contrast three dynamic econometric methodologies for estimating the demand for electricity by households and industrial companies. These are the Dynamic Regression Model and two approaches to cointegration (OLS and Maximum Likelihood). Since we find evidence of seasonal unit roots in the data we also test for seasonal cointegration. We find that the scale elasticities are similar in all three approaches but the OLS price elasticities are considerably lower. Moreover, OLS suggests non-cointegration. The paper concludes by stochastically simulating the DRMs to calculate upside-risk in electricity demand.
In the estimation of demand functions for energy resources, linear, log–linear and translog functional forms are commonly assumed. It has often been questioned whether such functional forms can indeed accurately represent the underlying relationships between the demand for various energy resources and explanatory variables such as energy prices, weather variables, income and other factors. This paper compares linear, log–linear and translog share equation functional forms against a non-parametric function. Bootstrapping methods are used to test the validity of using the three parametric functional forms in models of residential energy demand. Cross-sectional household-level data from the US BLS Consumption Expenditure survey and other government datasets are used. Each of the parametric functional forms tested performs poorly, suggesting that they may be insufficiently flexible to provide valid results in certain applications.
The aim of this paper is to describe the structure of the household’s energy demand as a discrete/continuous choice and, on this basis, establish an econometric model suitable for the data available in the Norwegian Energy Surveys. The discrete appliance choice is specified as a multinomial logit model, with a mixture of appliance attributes (operating costs) and individual characteristics (income, housing unit characteristics, etc.) as explanatory variables. In the next step the continuous choice of energy use is modelled conditional on the appliance choice. The energy prices turn out to be significant both when estimating the appliance choice and the conditional energy demand. The estimated price elasticity for energy exceeds minus unity. The paper discusses how this relatively strong price response should be interpreted in the context of other econometric analysis with no explicit appliance dependence. Finally, the significance of the many household characteristics at both stages of the model signals a high degree of heterogeneity within the households, which justifies the use of detailed micro-data in the modelling of the energy demand.
Unlike previous studies on the causal relationship between energy consumption and economic growth, this paper illustrates how the finding of cointegration (i.e. long-term equilibrium relationship) between these variables, may be used in testing Granger causality. Based on the most recent Johansen's multivariate cointegration tests preceded by various unit root or non-stationarity tests, we test for cointegration between total energy consumption and real income of six Asian economies: India, Pakistan, Malaysia, Singapore, Indonesia and the Philippines. Non-rejection of cointegration between variables rules out Granger non-causality and imples at least one way of Granger-causality, either unidirectional or bidirectionial. Secondly, by using a dynamic vector error-correction model, we then analyse the direction of Granger-causation and hence the within-sample Granger-exogeneity or endogeneity of each of the variables. Thirdly, the relative strength of the causality is gauged (through the dynamic variance decomposition technique) by decomposing the total impact of an unanticipated shock to each of the variables beyond the sample period, into proportions attributable to shocks in the other variables including its own, in the bivariate system. Results based on these tools of methodology indicate that while all pair-wise relationships shared common univariate integrational properties, only relationships for three countries (India, Pakistan and Indonesia) were cointegrated. For these countries, temporal causality results were mixed with unidirectional causality from energy to income for India, exactly the reverse for Indonesia, and mutual causality for Pakistan. The VDCs were not inconsistent with these results and provided us with an additional insight as to the relatively more dominant direction of causation in Pakistan. Simple bivariate vector-autoregressive models for the three non-cointegrated systems did not indicate any direction of causality, significantly in either direction.
This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized
method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our
GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation
in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables.
We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying
restrictions and Hausman specification tests.
Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/). This paper is an empirical investigation of the effects of climate on the use of electricity by consumers and producers in urban and rural areas within China. It takes advantage of an unusual combination of temporal and regional data sets in order to estimate temperature, as well as price and income elasticities of electricity demand. The estimated positive temperature/electric power feedback implies a continually increasing use of energy to produce electric power which, in China, is primarily based on coal. In the absence of countervailing measures, this will contribute to increased emissions, increased atmospheric concentrations of greenhouse gases, and increases in greenhouse warming. This study received funding from the MIT Joint Program on the Science and Policy of Global Change, which is supported by a consortium of government, industry and foundation sponsors.
Aggregate energy demand functions for 17 OECD countries are estimated with data for 1960-2003 using the Structural Time Series Model (STSM) thus allowing for a stochastic Underlying Energy Demand Trend (UEDT). It is found that the estimated long-run income and price elasticities range from 0.5 to 1.5 and -0.1 to -0.4 respectively. Furthermore the stochastic form for the UEDT is preferred for all countries suggesting a wide variation in the exogenous effects of energy saving technical progress in addition to other pertinent exogenous factors such as economic structure, consumer preferences, and socio-economic influences.
We explore a hypothesis that a change in investment behaviour among international oil companies (IOC) towards the end of the 1990s had long-lived effects on OPEC strategies, and on oil price formation. Coordinated investment constraints were imposed on the IOCs through financial market pressures for improved short-term profitability in the wake of the Asian economic crisis. A partial equilibrium model for the global oil market is applied to compare the effects of these tacitly collusive capital constraints on oil supply with an alternative characterised by industrial stability. Our results suggest that even temporary economic and financial shocks may have a long-term impact on oil price formation.
We use a dynamic general equilibrium model of the world economy to assess the economic implications of higher vulnerability from extreme meteorological events. In particular, we consider the impact of climate change on ENSO/NAO cycles, and the implied variation on regional expected damages, due to extreme events. We analyze how local impacts propagate inside the world economic structure, because of trade relationships among regions. Three effects are taken into account: (1) negative local shocks, determined by loss of resources, (2) changes in demand structure, generated by higher/lower precautionary saving, and (3) variations in regional economic growth paths
this paper and John Ham, David Hendry and members of the econometrics group at the London School of Economics for their useful comments on an earlier draft
A youth homicide reduction initiative in Boston in the mid- 1990s poses particular difficulties for program evaluation because it did not have a control group and the exact implementation date is unknown. A standard methodology in program evaluation is to use time series variation to compare pre- and postprogram outcomes. Such an approach is not valid, however, when the timing of program implementation or effect is unknown. To evaluate the Boston initiative, we adapt from the time series literature an unknown-breakpoint test to test for a change in regime. Tests for parameter instability provide a flexible framework for testing a range of hypotheses commonly posed in program evaluation. These tests both pinpoint the timing of maximal break and provide a valid test of statistical significance. We evaluate the results of the estimation using the asymptotic results in the literature and with our own Monte Carlo analyses. We conclude there was a statistically significant discontinuity in youth homicide incidents (on the order of 60%) shortly after the intervention was unveiled. Copyright (c) 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.
In this paper a set of ten different single-equation models of residential energy demand is being analyzed, derived by the imposition of linear parameter restrictions on a fairly general autoregressive distributed lag (ADL) model. Residential energy consumption is assumed to be explainable by households' real disposable income, movements in the real price of energy, and the temperature variable 'heating degree days.' In the empirical application, Austrian annual data for the period 1970 to 1992 are used. The main focus of the paper is on the control of the overall significance level of the tests based on the application of the closure test principle, introduced by Marcus, Peritz, and Gabriel (1976). The application illustrates nicely how one can, by defining a closed system of hypotheses, control the significance level alpha in supporting the search for a suitable specific model. The wide range of estimated elasticities, however, indicates that the estimation results depend strongly on the choice of the model specification.
This study presents a set of twelve monthly cross section regression analyses of the household demand for electricity. The methodology to be used is based upon a conditional demand framework which can be used to disaggregate the total household demand for electricity into the component demand functions for electricity through the media of particular appliances, even though no direct observations on specific appliance energy usage exist. The latter demand functions are used to estimate the monthly and annual average energy use for these appliances as well as the corresponding price and income elasticities.
This paper addresses the impact of endogenous technology through research and development (R&D) and learning by doing (LbD) on the timing of environmental policy. We develop two models, the first with technological change through R&D and the second with LbD. We study the interaction between environmental taxes and innovation externalities in a dynamic economy and prove policy equivalence between the second-best R&D and the LbD model. Our analysis shows that the difference found in the literature between optimal environmental policy in R&D and LbD models can partly be traced back to the set of policy instruments available, rather than being directly linked to the source of technological innovation. Arguments for early action in LbD models carry over to a second-best R&D setting. We show that environmental taxes should be high compared to the Pigouvian levels when an abatement industry is developing. We illustrate our analysis through numerical simulations on climate change policy.
In a world of certainty, the design of environmental policy is relatively straightforward, and boils down to maximizing the present value of the flow of social benefits minus costs. But the real world is one of considerable uncertainty -- over the physical and ecological impact of pollution, over the economic costs and benefits of reducing it, and over the discount rates that should be used to compute present values. The implications of uncertainty are complicated by the fact that most environmental policy problems involve highly nonlinear damage functions, important irreversibilities, and long time horizons. Correctly incorporating uncertainty in policy design is therefore one of the more interesting and important research areas in environmental economics. This paper offers no easy formulas or solutions for treating uncertainty -- to my knowledge, none exist. Instead, I try to clarify the ways in which various kinds of uncertainties will affect optimal policy design, and summarize what we know and don't know about the problem.
This paper discusses both distributional and allocational effects of limiting carbon dioxide emissions in a small and open economy. It starts from the assumption that Switzerland attempts to stabilize its greenhouse gas emissions over the next 25 years, and evaluates costs and benefits of the respective reduction program. From a methodological viewpoint, this paper illustrates, how a computable general equilibrium approach can be adopted for identifying economic effects of cutting greenhouse gas emissions on the national level. From a political economy point of view it considers the social incidence of a greenhouse policy. It shows in particular that public acceptance can be increased and economic costs of greenhouse policies can be reduced, if carbon taxes are accompanied by revenue redistribution. Copyright Kluwer Academic Publishers 1992
We examine an incentive scheme for a group of agents, where all agents are rewarded if the group meets its target. If the group does not meet its target, only the agents that meet their individual target are rewarded. In environmental policy, the EU burden sharing agreement and the UK Climate Change Agreements feature this incentive scheme. There is only a difference in outcome between group and individual rewards if emissions are stochastic. Group rewards generally lead to higher expected emissions than individual rewards. The attraction of the group reward scheme may lie in its fairness and its tough-looking targets.
During the ‘90s most Latin American countries were submitted to neoliberal structural reform policies. Neoliberal policies imposed market supremacy, reduced the State’s role in the economy and deregulated the markets. This paper aims at describing how these policies affected the most important macroeconomic indexes, with special emphasis on Argentina and Mexico, the two countries that suffered most from the economic crises of the ‘80s and ‘90s, and where the neoliberal policies were applied with greater orthodoxy. In spite of a slight improvement in some macroeconomic indexes, in Latin America neoliberalism failed to reduce poverty and unemployment, and was unable to guarantee a fair distribution of the wealth and improve welfare.
The state of anomie that has characterised and still characterises most Latin American countries, resulting from the fragmentation of the social fabric, has encouraged the rise of successful personalist leaderships in the ‘90s. This paper aims at investigating how neopopulism developed in Latin America, considering as main actors the two Presidents who have best embodied this ideal: Carlos Salinas de Gortari, (Mexico 1988-1994) and Carlos Menem (Argentina 1989-1999). Neopopulism is based on an economic project, the neoliberal policy based on cuts in the welfare, which seems very far from the populist positions of the past. Populism revives through the charisma of these Presidents, bypassing institutional or organisational forms of mediation between the leader and the masses. The development of selected social policies has gained strong political support from the lower classes, including extensive institutional reforms.
The paper provides new empirical evidence on the relationship between environmental efficiency and labour productivity using industry level data. We first provide a critical and extensive discussion around the interconnected issues of environmental efficiency and performance, firm performances and labour productivity, and environmental and non-environmental innovation dynamics. The most recent literature dealing with environmental innovation, environmental regulations and economic performances is taken as reference. We then test a newly adapted EKC hypothesis, by verifying the correlation between the two trends of environmental efficiency (productivity, namely sector emission on added value) and labour productivity (added value on employees) over a dynamic path. We exploit official NAMEA data sources for Italy over 1990-2002 for 29 sectoral branches. The period is crucial since environmental issues and then environmental policies came into the arena, and a restructuring of the economy occurred. It is thus interesting to assess the extent to which capital investments for the economy as a whole are associated with a positive or negative correlation between environmental efficiency of productive branches and labour productivity, often claimed by mainstream theory dealing with innovation in environmental economics. We believe that on the basis of the theoretical and empirical analyses focusing on innovation paths, firm performances and environmental externalities, there are good reasons to expect a positive correlation between environmental and labour productivities, or in alternative terms a negative correlation between mission intensity of production and labour productivity. The tested hypothesis is crucial within the long standing discussion over the potential trade-off or complementarity between environmental and labour productivity, strictly associated with sectoral and national technological innovation paths. The main added value of the paper is the analysis of th
Analyzing the risks of anthropogenic climate change requires sound probabilistic projections of CO2 emissions. Previous projections have broken important new ground, but many rely on out-of-range projections, are limited to the 21st century, or provide only implicit probabilistic information. Here we take a step towards resolving these problems by assimilating globally aggregated observations of population size, economic output, and CO2 emissions over the last three centuries into a simple economic model. We use this model to derive probabilistic projections of business-as-usual CO2 emissions to the year 2150. We demonstrate how the common practice to limit the calibration timescale to decades can result in biased and overconfident projections. The range of several CO2 emission scenarios (e.g., from the Special Report on Emission Scenarios) misses potentially important tails of our projected probability density function. Studies that have interpreted the range of CO2 emission scenarios as an approximation for the full forcing uncertainty may well be biased towards overconfident climate change projections.
Empirical research on the characteristics of environmentally responsive companies has focussed almost exclusively on US and Japanese firms. For Europe, which is commonly considered as the greenest of the three major developed economic markets, similar research is lacking. This paper seeks to fill this gap by empirically investigating the business and financial characteristics, stakeholder pressure and public policies distinguishing companies that have implemented the European Eco-Management and Audit System (EMAS) and those that have not using a unique firm-level dataset of European publicly quoted companies. The contribution of this paper is twofold. First of all, the decision to implement EMAS has not been widely analysed. Secondly, we focus on European firms which allows us to assess if and to what extent European firms behave like their US or Japanese counterparts. We find that the EMAS participation decision is positively influenced by the solvency ratio, the share of non-current liabilities and the average labour cost. Also, two measures of company size are positively associated with EMAS participation: both the absolute company size as well as the relative size of a company compared to its sector average. The profit margin on the other hand exerts a negative influence according to our results. We further show that public policy can heavily influence the EMAS participation decision: companies whose headquarters is located in a member state that actively encourages EMAS have a higher probability of participation.
In this paper we study the effect of international technology spillovers on carbon leakage. We first develop and analyse two simple competing models for carbon leakage. The first model represents the pollution haven hypothesis. It focuses on the international competition between firms that produce energy-intensive goods. The second model highlights the role of a globally integrated carbon-energy market. We calculate formulas for the leakage rates in both models and, through meta-analysis, show that the second model captures best the major mechanisms reported in the CGE literature on carbon leakage. We extend this model with endogenous energy-saving technology and international technology spillovers. This feature is shown to decrease carbon leakage. We build-in the endogenous energy-saving technology in a large CGE model and verify that the results from the formal model carry over. Carbon leakage becomes negative for moderate levels of international technology spillover.
Over the last few years, many studies have shown that social networks affect the socioeconomic development. This paper presents evidence, through the Italian microdata representative of the entire Italian population, that the quality and quantity of interpersonal relations of agents can increase their economic welfare. Two proxies of interpersonal relations at an individual level are considered: a proxy for the density and one for the quality of network structure of personal contacts. Both seem to have a positive effect on the level of household economic welfare of agents. This result proves robust to the inclusion of a variety of control variables and to the use of different econometric methods.
This paper proposed a methodological framework for the assessment of carbon stocks and the development and identification of land use, land use change and land management scenarios, whereby enhancing carbon sequestration synergistically increases biodiversity, the prevention of land degradation and food security through the increases in crop yields. The framework integrates satellite image interpretation, computer modelling tools (i.e. software customization of off-the-shelf soil organic matter turnover simulation models) and Geographical Information Systems (GIS). The framework addresses directly and indirectly the cross-cutting ecological concerns foci of major global conventions: climate change, biodiversity, the combat of desertification and food security. Their synergies are targeted by providing procedures for assessing and identifying simultaneously carbon sinks, potential increases in plant diversity, measures to prevent land degradation and enhancements in food security through crop yields, implicit in each land use change and land management scenario. The scenarios aim at providing “win-win” options to decision makers through the framework’s decision support tools. Issues concerning complex model parameterization and spatial representation were tackled through tight coupling soil carbon models to GIS via software customization. Results of applying the framework in the field in two developing countries indicate that reasonably accurate estimates of carbon sequestration can be obtained through modeling; and that alternative best soil organic matter management practices that arrest shifting “slash-and-burn” cultivation and prevent burning and emissions, can be identified. Such options also result in increased crop yields and food security for an average family size in the area, while enhancing biodiversity and preventing land degradation. These options demonstrate that the judicious management of organic matter is central to greenhouse gas mitigation and the attainment of synergistic ecological benefits, which is the concern of global conventions. The framework is to be further developed through successive approximations and refinement in future, extending its applicability to other landscapes.
This paper is a first attempt to investigate the effect of climate on the demand for different energy vectors from different final users. The ultimate motivation for this is to arrive to a consistent evaluation of the impact of climate change on key consumption goods and primary factors such as energy vectors. This paper addresses these issues by means of a dynamic panel analysis of the demand for coal, gas, electricity, oil and oil products by residential, commercial and industrial users in OECD and (a few) non-OECD countries. It turns out that temperature has a very different influence on the demand of energy vectors as consumption goods and on their demand as primary factors. In general, residential demand responds negatively to temperature increases, while industrial demand is insensitive to temperature increases. As to the service sector, only electricity demand displays a mildly significant negative elasticity to temperature changes.
This paper investigates the relationship between outside air temperature and the residential demand for space heating energy. These nonlinearities are investigated empirically using high frequency panel data for a sample of U.K. households, and both parametric and nonparametric methods for identifying nonlinearities are examined. The econometric evidence finds support for important nonlinearities across the range of observed temperatures and points to limitations in the use of parametric functional forms. Copyright 1997 by Blackwell Publishing Ltd
In many markets, changes in the spot price are partially predictable. We show that when this is the case: (1) although unbiased, traditional regression estimates of the minimum variance hedge ratio are inefficient, (2) estimates of the riskiness of both hedged and unhedged positions are biased upward, and (3) estimates of the percentage risk reduction achievable through hedging are biased downward. For natural gas cross hedges, we find that both the inefficiency and bias are substantial. We further find that incorporating the expected change in the spot price, as measured by the futures-spot price spread at the beginning of the hedge, into the regression results in a substantial increase in efficiency and reduction in the bias.
This paper utilizes an international panel data set and a dynamic demand specification for gasoline to compare the performance of homogeneous and heterogeneous parameter estimators. In addition to comparing the plausibility of the various estimates, a forecast performance comparison is performed to examine differences in predictions over one-, five-, and ten-year horizons.
This paper proposes unit root tests for dynamic heterogeneous panels based on the mean of individual unit root statistics. In particular it proposes a standardized t-bar test statistic based on the (augmented) Dickey–Fuller statistics averaged across the groups. Under a general setting this statistic is shown to converge in probability to a standard normal variate sequentially with T (the time series dimension) →∞, followed by N (the cross sectional dimension) →∞. A diagonal convergence result with T and N→∞ while N/T→k,k being a finite non-negative constant, is also conjectured. In the special case where errors in individual Dickey–Fuller (DF) regressions are serially uncorrelated a modified version of the standardized t-bar statistic is shown to be distributed as standard normal as N→∞ for a fixed T, so long as T>5 in the case of DF regressions with intercepts and T>6 in the case of DF regressions with intercepts and linear time trends. An exact fixed N and T test is also developed using the simple average of the DF statistics. Monte Carlo results show that if a large enough lag order is selected for the underlying ADF regressions, then the small sample performances of the t-bar test is reasonably satisfactory and generally better than the test proposed by Levin and Lin (Unpublished manuscript, University of California, San Diego, 1993).