Corso di Laurea Magistrale
in Economia e Finanza
”ordinamento ex D.M. 270/2004”
Tesi di Laurea
Does climate influence
households’ thermal comfort
decisions?
Evidence from the 2011 OECD EPIC survey
Primo Relatore
Ch. Prof.ssa Enrica De Cian Secondo Relatore
Ch. Prof.ssa Teresa Randazzo Laureando
Filippo Pavanello Matricola 850219 Anno Accademico 2017/2018
Acknowledgments
For my final dissertation I would like to express my deep gratitude to Prof. Enrica De Cian, my first supervisor, for her patient guidance, enthusiastic encouragement and useful critiques. I would also like to thank Prof. Teresa Randazzo, my second supervisor, for her advice, feedback and assistance dur-ing this thesis work. My grateful thanks are also extended to the other EN-ERGYA team members; especially, to Dr. Malcolm Mistry for the merge with the climate data and for the country plots, and to Dr. Marinella Davide for her help in the data merge in Stata.
I would also like to offer my special thanks to Prof. Maria Cristina Moli-nari for giving to me the opportunity of being the Lecturer Assistant of her bachelor module, Industrial Organisation, for two consecutive years during my Master’s degree.
Last but not least, I wish to acknowledge my family and my friends for their support during my studies.
Abstract
This dissertation investigates how households have been adapting to climate change through the use of two technologies important for thermal comfort, air conditioning and thermal insulation. Merging the 2011 OECD EPIC survey and a global gridded dataset of histori-cal temperatures in a cross-sectional dataset, I study the determinants of installing air conditioning or of adopting thermal insulation in re-sponse to a warmer climate in eight countries, exploting cross-sectional variation. The study also controls for a set of demographic, socio-economic and attitudinal variables that may affect adoption decisions as well. The econometric analysis is based on a binary probit model. The empirical results suggest that exposure to a warmer climate in-fluences only air conditioning adoption, whereas climatic conditions seem not to affect thermal insulation decisions. In addition, wealth, housing characteristics, environmental and energy-saving attitude and presence of minors in the household drive the cooling technology dif-fusion. Thermal insulation installation mainly depends on wealth, dwelling characteristics, age, household size and propensity to energy-saving behaviours. There is instead no evidence of a possible joint decision for the two technologies.
Contents
List of Tables 8
List of Figures 9
1 Introduction 11
1.1 Mitigation and adaptation . . . 13
1.2 Intensive and extensive margin . . . 14
1.3 The 2011 OECD EPIC Survey . . . 15
1.4 Weather data . . . 17
2 Literature Review 19 2.1 The new-climate economy literature . . . 19
2.2 Climate change impact on the adoption of air conditioners . . 24
2.3 Previous analyses of the OECD 2011 EPIC survey . . . 28
3 Thermal comfort and climate: a new empirical analysis 31 3.1 Data and methodology . . . 31
3.1.1 The Global Land Data Assimilation System (GLDAS) 31 3.1.2 Combining climate gridded data and the OECD 2011 EPIC survey . . . 32
3.1.3 The wealth index . . . 34
3.2 Descriptive statistics and t-tests . . . 38
3.2.1 Descriptives for each country-sample . . . 38
3.2.2 Descriptives for income groups . . . 42
3.2.3 T-tests . . . 45
3.3 The empirical framework . . . 49
3.4 Research hypotheses . . . 53
4 Results 69 4.1 Full sample . . . 69
4.1.1 Air conditioning adoption . . . 69
4.1.2 Thermal insulation adoption . . . 76
4.2 By country . . . 79
4.3 By income group . . . 85
5 Conclusion 91
A Appendix 104 A.1 CDD country maps . . . 104 A.2 HDD country maps . . . 108
List of Acronyms
AC Air conditioning (also air conditioner) CDD Cooling Degree Days
CO2 Carbon dioxide
CRU Climate Research Unit
EEA European Environment Agency
EPIC Environmental Policy and Individual Behaviour Change EU Europe
GDAM Database of Global Administrative Area GHCN Global Historical Climatology Network GHG Greenhouse Gases
GIS Geographic Information System
GLDAS Global Land Data Assimilation System HDD Heating Degree Days
IEA International Energy Agency
IPCC Intergovernmental Panel on Climate Change NGO Non-Governmental Organisation
NON-EU Non-Europe
OECD Organisation for Economic Co-operation and Development OLS Ordinary Least Squares
PCA Principal Component Analysis POD Proper Orthogonal Decomposition RSS Remote Sensing Systems
SES Social Economic Status
SRES Special Report on Emission Scenarios TI Thermal insulation
UNFCCC United Nations Framework Convention of Climate Change USA United States of America
List of Tables
1 PCA results for the wealth index . . . 37
2 Description of variables . . . 38
3 Summary statistics by country for all variables . . . 39
4 Income groups’ statistics for wealth index . . . 43
5 Summary statistics by income group for all variables . . . 45
6 T-tests for all variables by air conditioning adoption . . . 47
7 T-tests for all variables by thermal insulation implementation 48 8 Univariate probit regressions for full sample. Dependent vari-able: the adoption of air conditioning . . . 69
9 Probit regression. Full sample. Wealth index regression. Im-pact of an increase in CDD on the probability of adopting AC . . . 71
10 Univariate probit regressions for full sample. Dependent vari-able: the adoption of thermal insulation . . . 76
11 Univariate probit regression by country group. Dependent variable: the adoption of air conditioning . . . 80
12 Univariate probit regression by country group. Dependent variable: the adoption of thermal insulation . . . 83
13 Univariate probit regressions by income group. Dependent variable: the adoption of air conditioning . . . 85
14 Univariate probit regressions by income group. Dependent variable: the adoption of thermal insulation . . . 88
List of Figures
1 Exemplification of the data merging . . . 34
2 Correlation plot between income and wealth index . . . 37
3 The household’s air conditioning decision . . . 50
4 Correlation plots between air conditioning adoption and
cli-mate variables . . . 55
5 Correlation plots between thermal insulation adoption and
cli-mate variables . . . 56
6 Correlation plot between air conditioning adoption and income 58
7 Correlation plot between air conditioning adoption and wealth
index . . . 58
8 Correlation plot between thermal insulation adoption and
in-come . . . 59
9 Correlation plot between thermal insulation adoption and wealth
index . . . 59
10 Correlation plot between air conditioning adoption and living
in an urban area . . . 61
11 Correlation plot between thermal insulation adoption and
liv-ing in an urban area . . . 61
12 Correlation plots between air conditioning adoption and house
characteristics . . . 63
13 Correlation plots between thermal insulation and house
char-acteristics . . . 64
14 Correlation plots between air conditioning adoption and
en-ergy and environmental behaviour and attitude . . . 66
15 Correlation plots between thermal insulation adoption and
en-ergy and environmental behaviour and attitude . . . 67
16 Correlation plot between air conditioning adoption and
ther-mal insulation installation . . . 68
17 Predictive margins. Air conditioning saturation vs CDD. Wealth
index regression. All covariates at mean level . . . 72
18 Predictive margins. Interaction term: CDDxHDD. Dependent
variable: AC adoption. Wealth index regression from Table 8 . 73
19 Predictive margins. Interaction term: CDDxHDD. Dependent
variable: AC adoption. Income regression from Table 8 . . . . 73
20 Maps of cooling degree days (CDD) for Australia and Canada 104
21 Maps of cooling degree days (CDD) for France and Japan . . . 105
22 Maps of cooling degree days (CDD) for Netherlands and Spain 106
23 Maps of cooling degree days (CDD) for Sweden and Switzerland107
25 Maps of heating degree days (HDD) for France and Japan . . 109
26 Maps of heating degree days (HDD) for Netherlands and Spain 110
1
Introduction
According to IPCC Working Group 1 (2013)[54] at the end of the 21st cen-tury surface average temperature worldwide is projected to likely exceed 1.5
◦C relative to 1850 to 1900. The main cause is the rising concentration of
greenhouse gases (GHG) in the atmosphere, namely carbon dioxide (CO2),
methane and nitrous oxide. Especially, human activity have increased CO2
concentrations by 40% since pre-industrial levels, reaching unprecedented levels.
The consequences of global warming for human systems will be dangerous. Glaciers melting, sea-level rise, higher frequency of extreme weather events, loss of biodiversity, health risks and other events will modify people’s life patterns. The risks deriving from climate change can be reduced only by limiting both its intensity and its rate (IPCC Working Group 2, 2014[55]). In response to climate change, scholars and policy institutions have recently led analyses to point out specific policies helpful for mitigating its impact. However, mitigation measures are not sufficient. Policy makers and people have already started to implement adaptation activities to cope with the changes we already observed. Global average temperature has increased by
almost 1 ◦C already since the pre-industrial revolution. While mitigation is
needed in order to limit future warming and to manage the risks of climate change, adaptation strategies are also be required to cope with the climate that has already changed. Targeted policies also based on future adaptation responses, such as improving the energy efficiency of buildings and appli-ances, might reduce both the impacts and the mitigation costs of changes in climate conditions.
My dissertation aims at studying how households adapt to a warmer cli-mate by adopting energy-using durable goods that provide thermal comfort services. By addressing this topic, this thesis contributes to highlight the potential risks associated with some adaptation strategies that rely on en-ergy. A topic that is receiving a lot of attention. Indeed energy and climate are strongly related. Energy is one of the main sources of GHG emissions. However, at the same time, it might also be a main adaptation strategy in response to global warming. To increase their thermal comfort, households might adapt to higher temperatures by increasing their demand for resi-dential buildings’ cooling. According to a new report by the International Energy Agency (IEA), the Future of Cooling (IEA, 2018[52]), space cooling is indeed the fastest growing energy service in buildings and the main technol-ogy which addresses this rising demand is air conditioning (AC). The steady increasing diffusion of air conditioning is one of the most critical blind spots in today’s energy debate (IEA, 2018[52]). However, even if air conditioning is
the most common and known cooling technology to cope with higher temper-atures, it is not the only one available. A further technology that households might adopt is thermal insulation (TI) of walls and/or roof. In buildings thermal insulation is not only used for space heating, but also for passive space cooling. Contrary to air conditioning, thermal insulation is an immo-bile energy-efficient technology that reduces buildings’ energy consumption and waste, and, consequently, the emissions of GHG. An increasing adoption of this technology would be crucial to reduce the environmental costs deriv-ing from the risderiv-ing demand for space coolderiv-ing.
In order to understand how climatic conditions influence thermal comfort decisions, I combine the Environmental Policy and Individual Behaviour
Change (EPIC) survey1, conducted by the Organisation for Economic
Co-operation and Development (OECD), with a global gridded dataset of histor-ical temperatures obtained from the Global Land Data Assimilation System
(GLDAS2). The cross-sectional dimension of the dataset makes it possible
to capture households’ long-run adaptation responses. The aim is to anal-yse the determinants of the decision of adopting air conditioning or thermal insulation in the primary residence in response to a warmer climate, while controlling for a set of demographic, socio-economic and attitudinal variables that might also influence adoption decisions. I am also interested to study whether households might combine the adoption of the two technologies. As measure of the typical intensity and duration of hot and cold climate I use long-term average cooling degree days (CDD) and heating degree days (HDD) over the period 1986-2011. Both CDD and HDD are variables derived from measurements of outside air temperature (EEA, 2016[39]).
The dissertation develops as follows. Section 1 introduces some key con-cepts related to climate change policy and summarises the novel feature of the data used. Section 2 reviews the new climate-economy literature and the previous studies using the OECD 2011 EPIC survey dataset. Section 3 out-lines the methodological approach used to conduct my analysis, explaining both the empirical model and the data used for the econometric analysis. Section 4 presents the empirical results of the econometric analysis, along with their interpretation and discussion. Section 5 concludes.
1One of the main reference on the 2011 OECD EPIC survey is the following, OECD
(2014)[80]
1.1
Mitigation and adaptation
When we talk about policies for climate change we have to distinguish two main different approaches, mitigation and adaptation.
The United Nations Framework Convention of Climate Change (UNFCCC) defines mitigation as the set of all those human measures and activities opted for ”limiting anthropogenic emissions of greenhouses gases and protect-ing and enhancprotect-ing greenhouses gas sinks and reservoirs” (UNFCC, 1992)[89]. An example of mitigation option is the decarbonisation of energy supply sec-tor, reaching low- or zero-carbon emissions to produce electricity.
Adaptation, instead, is defined by Intergovernmental Panel on Climate Change (IPCC) as ”the process of adjustment to actual or expected climate and its effects. In human systems, adaptation seeks to moderate or avoid harm or exploit beneficial opportunities. In some natural systems, human intervention may facilitate adjustment to expected climate and its effects” (IPCC, 2014[56]). The adoption of air conditioning is an example of adapta-tion opadapta-tion applied by human systems in order to avoid, for instance, health issues due to a warmer climate.
As Klein et al. (2005)[62] suggest, the two approaches have three main
differences. First, they differ in both spatial and temporal ranges of their effectiveness. While mitigation options are generally global and effective af-ter decades in reducing GHG concentrations, most adaptation options are locally distributed and effective on a shorter time scale compared to miti-gation, since changes in climate are already occurring. Second, adaptation produces benefits which are difficult to measure, as there is not a single metric and their intensity varies in accordance to the local context where they oc-cur. Mitigation policies generate, instead, global benefits which, specifically, aim to reduce GHG emissions and are easily measurable. Third, mitiga-tion policies result to involve a more limited number of actors and sectors, and, hence, of interests. Its extent is generally limited to energy, trans-port, forestry and agriculture, since these sectors are strictly related with GHG emissions. This eases mitigation policies’ planning and implementa-tions. Contrariwise, adaptation has a greater extent since it is also connected with public goods. Agriculture, biodiversity, human health, energy, urban planning, water provision, resource depletion and tourism are some of the vast number of sectors, which adaptation activities cover. Consequently, the decisions must be taken at many different levels, since the involved private and public actors are several. This usually leads policymakers not to include the impacts of possible adaptation activities (Klein et al, 2005[62]). Nev-ertheless, despite the divergences, scholars suggest that the two approaches
should be applied in synergy. Neither is singularly able to deal with climate change. Mitigation deals with the causes, namely reducing emissions today in order to limit the extent of climate change in the future. Adaptation deals with the consequences of climate change, for example with the changes in temperature, including its variability (Bosello et al., 2013[17])
1.2
Intensive and extensive margin
Focusing on adaptation, the literature has defined different types of adap-tation strategies. For instance, on the one hand, people might adapt to a weather shock autonomously responding to price signals (reactive
adapta-tion3). On the other hand, local governments can plan adaptation
invest-ments ahead, building infrastructure that will shield local population over a
longer period of time (proactive adaptation4).
A key strategy used by people in order to autonomously respond to a shock in temperature such as heat waves, is energy demand, or more specifically, the use of heating and cooling services.
Since energy demand is a derived demand for energy services, meaning that it is conditional on the availability of durable goods that transform the en-ergy (e.g. electricity) into a service (e.g. thermal comfort), the literature has distinguished two margins of change: the intensive and the extensive margin. The intensive margin refers to changes in the intensity of energy use, con-ditional on a specific choice regarding energy-using stock and appliances. It represents the change in variable energy, for example in response to temper-ature. This is a short-run change. The first derivatives necessarily explain this measure (Auffhammer and Mansur, 2014[10]):
∂W ∂t = ~P 0 E ∂ ~E(F0) ∂t
where W is the welfare, ~PE represents energy prices, F0 is the current
distri-bution of outdoor temperature, t is the realised outdoor temperature, ~E is
a vector of energy sources like oil, electricity, natural gas, and coal. A sim-ple examsim-ple is the effect of higher temperatures on the electricity demand
3Reactive adaptation coincides with the short-term responses to change in climatic
conditions (e.g. increasing use of air conditioning). It is easier and cheaper to adopt, but not necessarily the best response.
4Proactive adaptation represents the long-run decisions to cope with climate change
(e.g. thermal insulation adoption). These responses are likely to be more effective than reactive ones. However, they are less exploited and, therefore, they need more policy support.
(measured in kWh), conditional on the households’ choice of adopting air conditioning. As we will see in Section 2, most of the existing literature has indeed focused on the intensive margin, using either panel data or time-series approaches to estimate the short-run sensitivity of energy demand to weather shocks, keeping constant the stock of energy-using capital.
The literature on the extensive margin is less developed than that one of the intensive margin. The extensive margin refers to shifts in specific de-cisions regarding energy-using capital, typically, over a longer period of time. It is not a matter of intensity, but it is often related to a discrete choice con-cerning for example energy-using appliances. Therefore, the first derivatives are not necessarily able to explain this measure. A general example is the effect of changes in climate conditions on the decision of adopting air con-ditioning. Other examples include shifts in households’ purchasing choices, from fuels to building characteristics to energy technologies. For instance, Mansur et al. (2008)[67] estimate that climate change will likely cause a fuel switching in US. As we will see in Section 2, some contributions on the ex-tensive margin use cross-sectional estimates, since they provide very long-run equilibrium. Some other works use panel estimates. Especially, they, first, estimate elasticities after short-run weather shocks (intensive margin) and, then, using the latters, the long-run adaptation decisions (extensive margin).
1.3
The 2011 OECD EPIC Survey
The 2011 Environmental Policy and Individual Behaviour Change (EPIC) is a survey conducted by the Organisation for Economic Co-operation and Development (OECD). The recipients of this survey are households. The aim is to provide information about demand side for policy makers. Studies on the household environmental behaviour and attitude have necessarily to be taken account for implementing effective policies. Actually, two EPIC surveys have been conducted, namely one in 2008 and one in 2011. Specif-ically, for my analysis I use the 2011 OECD EPIC survey, because it is a more extended version of the first wave conducted in 2008. It contains more countries and households are geocoded (latitude and longitude), making it possible to merge it with climate data.
The 2011 OECD EPIC survey involved eleven countries (Australia, Canada, Chile, France, Israel, Japan, Korea, Netherlands, Spain, Sweden and Switzer-land), collecting 12202 participants. The survey consists of an online ques-tionnaire and is structured in six sections. Each of them is composed by a series of questions on the specific topic related to the section.
• Part A: Socio-demographic characteristics (e.g. gender, age, re-gion, employment status, occupation, income, home size, home type); • Part B: Attitudinal characteristics (e.g. environmental concerns,
environmental attitudes, membership in a NGO, membership in an en-vironmental NGO, enen-vironmental knowledge, enen-vironmental satisfac-tion);
• Part C: Waste (e.g. frequency of mixed waste collection, amount of mixed waste produced, knowledge about the recycling process, waste collection services available, disposal of medicine and electronic equip-ment, cost for collection services, government actions);
• Part D: Transport (e.g. number of owned/used cars, hybrid/electric/flex-fuel cars ownership, number of owned/used motorcycles, factors in-fluencing cars purchase, willingness to pay for an electric car, public transport efficiency, effectiveness and cost, government actions); • Part E: Energy (e.g. electricity consumption, electricity expenditure,
energy sources, adoption of energy conservative measures, adoption of specific appliances, energy use behaviours, willingness to pay for renewable energy);
• Part F: Food (e.g. factors influencing food shopping choices, food consumption behaviours, food shop type, food thrown away, willingness to pay for fresh fruits and vegetables);
• Part G: Water (e.g. water use behaviours, how water bill is calculated, drinking water habits, investments in water saving).
For the purpose of my analysis, I use variables based on the data collected in three survey parts: Socio-demographic characteristics, Attitudinal char-acteristics and Energy.
Note that the survey was constructed through both stratification and quota sampling methods, trying to make each country-sample the most represen-tative of the related nation. For each sample, OECD sets country-specific quota targets based on statistics provided by national agencies. Strat-ification and quota targets were imposed for four variables, namely gender, age, region and income. For gender the aim was about half male and half female for all the surveyed countries. Age was stratified, identifying five age
groups: 18 to 24, 25 to 34, 35 to 44, 45 to 54 and 55 to 69. Region was strat-ified as well. However, quotas was built for macro-regions, which include
three to five regions5. Stratification of income was obtained for each country
estimating households’ after-tax income quintile. Then, filling the survey, each household chose its income category. Households kept filling the sur-vey until the quotas were reached. When a quota was reached, a household, which had that characteristic, was stopped completing the questionnaire (see Annex B in OECD, 2014[80]). For my analysis the initial sample has been reduced to a smaller sub-sample. Therefore, I checked that quota targets were respected. Since quota targets were confirmed, the sub-sample resulted to be representative of the initial sample and the introduction of weights in the analysis was not necessary. In the 2011 OECD EPIC survey there was also the possibility that households might have not known the answer for some questions (e.g. ”In your household, do you pay for your electricity according to how much electricity you use? (e.g. individual electricity me-tering)? ”(OECD, 2014[80])). For these questions OECD provided a ”do not know” option. As consequence, there may be loss of sample when researchers focus on variables related to these questions. However, for a discrete choice, like having air conditioners, this possible issue is drastically reduced.
1.4
Weather data
Along with the 2011 OECD EPIC data, weather data are another key el-ement in my analysis. They are necessary to study the effect of climate change on socio-economic variables. Scholars have exploited different types of weather data. There currently exists four types of weather data, distin-guished according to their source (see Dell et al., 2014[34]; Auffhammer et al., 2013[8]).
The ground stations data are weather data collected by a ground weather station present in a specific location. An example is the Global Historical Climatology Network (GHCN). They work pretty well in regions where there is a deep-rooted and dense network of weather stations. They also provide highly-reliable results for the locations where they are set. However, they are not reliable when the study objects are areas with a sparse coverage, especially poorer countries (Dell et al., 2014[34]).
The satellite measurements are weather data captured by satellites. An ex-ample is the Remote Sensing Systems (RSS). With respect to ground stations
data they are more aggregated, with a 2.5◦ x 2.5◦ resolution. Therefore, a
5For instance, 11.0% of French households has to come from the Sud-Ouest. This
macro-region includes the following regions Aquitaine, Limousin, Midi-Pyr´en´ees and Poitou-Charentes.
weather station collects for its location more accurate data. Moreover, since satellites are basically newly-built, they cannot provide far back in time his-torical series. They, instead, provide useful information on those areas with a low ground weather station coverage (Dell et al., 2014[34]).
Another reliable way to deal with a low ground weather stations coverage is using the gridded data. Through an interpolation among the ground stations data, they provide a ”balanced panel of weather data for any point on a grid ” (Dell et al., 2014[34]). In this way, they adjust issues like low coverage and elevation biases. An example is the Climate Research Unit (CRU). Never-theless, they present some drawbacks. First, different interpolation methods may lead to different estimates. This is a significant disadvantage for a vari-able, like precipitation, which greatly varies geographically. Second, a further challenge may come from the presence of more grid cells than related weather stations. This might become an issue when the research interest is devoted to small geographic areas (Dell et al., 2014[34]).
The last kind of weather data are the reanalysis data. They are obtained through a data assimilation process. Data assimilation is a process which provides weather gridded data through the combination of observation-based data, coming from different sources like ground stations and satellites, and earth physics-based models (Auffhammer et al., 2013[8]). Contrary to previ-ous general gridded data, reanalysis data are not the result of an interpolation system but of a climate model. Similarly, they are useful to increase weather data in areas with a low coverage. However, since they are based on models, they might not reflect the exact true climate (Dell et al., 2014[34]; Auffham-mer et al., 2013[8]). An example is the dataset I use for my analysis, the Global Land Data Assimilation System (GLDAS).
2
Literature Review
A new emerging literature in the economic discipline, the climate-economy literature, has been investigating on how changes in the climate have been affecting socio-economic outcomes. Some studies focus on historical impacts, some others use the evidence from the past to infer future consequences of climate change. Several propagation channels through which climate could affect our society have been explored, e.g. energy, agriculture, labour pro-ductivity, industrial and services output, political stability, crime, health and mortality.
The following section proposes a review of this new climate-economy liter-ature with a focus on a research gap, adaptation, which is also where this thesis provides its contribution. According to Burke et al. (2016)[21], adap-tation indeed represents the climate-economy literature’s research frontier which has the highest potential, also because there are still few contribu-tions.
The section is subdivided in the following way. In order to navigate the sea of peer-reviewed papers on this topic, I first propose a categorisation of the existing contributions. Following this classification, I present examples of the different kind of analyses, which scholars have explored to study the cli-mate change economic effects (2.1). Then, focusing on energy as propagation channel, my attention is oriented to the studies on energy consumption for cooling. Specifically, the focus is on the air conditioning (AC) adoption in re-sponse to warmer temperatures due to climate change (2.2). I do not review contributions on thermal insulation adoption, since the only well-developed literature on the extensive margin responses is that one on air conditioning (Auffhammer and Mansur, 2014[10]). Finally, given the prominent role of the 2011 OECD EPIC survey in my dissertation, I review the previous contribu-tions of interest that use that same dataset (2.3). As result of this section, the cumulative contribution of this work is identified.
2.1
The new-climate economy literature
Independently from the propagation channel, scholars (e.g. Dell et al, 2014[34]; Auffhammer and Mansur, 2014[10]) categorise contributions in the climate-economy literature by the type of data exploited in the analyses, namely cross-sectional, time-series and panel data, and, therefore, by the source of identification used to identify the effect of temperature or other climatic vari-ables. Each kind of data has its own advantages and disadvantages.
the ability to reflect households’ long-run behaviours and adjustments. How-ever, there also clear empirical drawbacks. Scholars (e.g. Mendelsohn and Massetti, 2017 [77]; Auffhammer and Mansur, 2014[10]; Dell et al., 2014[34]) identify three main disadvantages. First, cross-section results might lead to biased coefficients because of omitted variables correlated with both the
vari-ables of interest and the climate varivari-ables (see Deschˆenes and Greenstone,
2007[35]). Second, interpretation issues might occur, since the long-run vari-ables used in the analysis, such as climate varivari-ables, might not coincide with their future expected distributions in each geography area examined. Third, another issue might come from the long-run nature of cross-sectional data. In fact, cross-section estimates might include historical mechanisms which will unlikely occur in the future. Cross-sectional approaches have been mainly used to study climate change impacts on agriculture and energy.
Mendelsohn et al. (1994)[73] study the impact of climate change on
agricul-ture6, specifically on land prices and farm revenues. The results point out
that farmland values per acre are non-linearly influenced by temperature and precipitations, with degrees of influence varying across seasons.
Moving to energy, Mansur et al. (2008)[67] estimate that climate change will
likely cause a fuel switching in US7. Households will spend more in
electric-ity consumption for cooling and less for other fuels (e.g. natural gas, fuel oil, LPG and kerosene) typically used for heating. Moreover, in the warmer US countries consumers will tend to substitute other fuels (such as gas or oil) with electricity, since electricity is resulted more attractive than a com-bination of electricity and other fuels. In these warmer US countries the expenditures will be higher, since the further costs for cooling will exceed the decrease in heating costs. As consequence, they predict net damages to
the US energy sector8.
Wolfram et al. (2012)[94] suggest that economic growth, especially the in-come rises of poor-medium households, will be a main driver of energy de-mand growth in the long-run, since also poorer people will start buying appliances, like air conditioners, refrigerators, vehicles.
6They use a so-called Ricardian approach: ”this approach takes an underlying
pro-duction function and estimates impacts by varying one or a few input variables, such as temperature, precipitation, and carbon dioxide levels”(Mendelsohn et al., 1994[73]).
7They estimate a fuel choice model through a multinomial discrete-continuous logit
model, based on the Dubbin-McFadden approach (see Dubin and McFadden, 1984[38]).
8Simulating the inter-temporal effects of different climate scenarios (2000
Intergovern-mental Panel on Climate Change Special Report on Emissions Scenarios (SRES)[53]), higher population growth, higher income growth) on energy expenditures for 2100, the authors predicts welfare losses in all scenarios. For instance, the base case (warmer tem-peratures by 5◦C in 2100) forecast a welfare loss of about 57$ billion.
According to Auffhammer and Mansur (2014)[9], contributions using time-series data are not so informative. In contrast with cross-sectional data, time-series data are able to only capture intensive margin, namely short-run adjustments to climate change. Similarly, omitted variables are a common is-sue. Indeed, since time-series data analyses require aggregate variables, some factors, which compose them, might remain unobserved, generating biased coefficients.
Considine (2000)[28] examines the effect of changes in weather on energy
de-mand and CO2 emissions in US. The author estimates short-run elasticities
with respect to price, income and weather proxied by heating degree days (HDD) and cooling degree days (CDD). Contrary to the previous literature, he finds HDD elasticity higher than CDD elasticity. Consequently, the
pa-per suggests that a warmer climate has slightly reduced the CO2 emissions
in USA. Warmer weather reduces the energy demand for heating, which at least equipoises the increase in energy demand for cooling.
Finally, panel data have the main advantage of significantly reducing the omitted variable issue. They allow for addressing changes in unobservable factors. As Dell et al. (2014)[34] highlight, it has become the preferred approach, because of its stronger properties at identifying casual effects. Scholars usually identify short-run impacts of weather shocks, in order to investigate variations of the variables of interest over time and within a spe-cific spatial entity. Through estimates gathered from short-run analyses, scholars can then investigate either the long-run adaptation choices or the
long-run impacts using different climate scenarios9. However, both Dell et
al. (2014)[34] and Auffhammer and Mansur (2014)[10] suggest that, with respect to cross-sectional data, panel data have an interpretative issue. On the one hand, they are not able to express the effects of long-run fluctuations of the climatic variables. The result of a short-run weather shocks may be pretty different from that one of a permanent change in climate, because of long-run adaptation processes. This means that adaptation might lead to an overestimation of weather shocks effects with respect to the ones of climate change. On the other hand, changes in climatic conditions may also generate damages that cannot be internalised in short-run weather shocks. This intensification issue might induce an underestimation of weather shocks effects with respect to those ones of climate change. Another issue for panel data analyses is related to general equilibrium effects. Some general macroe-conomic variables (such as capital accumulation) might be adjusted in the long-run. As consequence, in this case as well, the climate change long-run
effects computed from short-run estimates might decrease. The range of ap-plication of panel approaches varies from agriculture to income, mortality, labour productivity, energy, social issues.
Deschˆenes and Greenstone (2007)[35] estimate the effect of yearly plausible
random fluctuations of precipitations and temperatures on farmland profits in US. While in the short-run there is no significant correlation between weather and land profits, the authors find that in the long-run climate change will yearly increase agricultural profits by 4%. The expected rise in temperatures and precipitations does not have an effect (short-run prices fluctuations) on land values. The increase in land profits is instead due to long-run adapta-tion to climate change.
Graff-Zivin and Neidell (2014)[45] study the effect of climate change on labour productivity. They find two main adaptation results. First, warmer tem-peratures lead employers highly exposed to climate to be less productive. Second, climate change induces unemployed people to prefer indoor leisure rather than outdoor leisure.
Deschˆenes and Greenstone (2011)[36] investigate the correlation between
mortality and climate change in USA, studying the welfare costs for health generated by the effect of yearly plausible random fluctuations of temper-atures. The study highlights a clear increase in mortality due to warmer
temperatures10. Moreover, they find that climate change will generate a
sig-nificant increase in energy consumption from residential sector, estimating a U-shaped relationship between energy demand and temperature.
Auffhammer and Aroonruengsawat (2011)[11] also broadly find a U-shaped
curve, describing the relationship. Simulating two scenarios11 of warmer
temperatures until 2100, they study the economic impact of climate change on both residential energy demand (especially, on prices) and population in California. The results suggest an increasing energy consumption over years, with growth varying across the different Californian climate zones. The rela-tionship varies from flat curve to the U-shaped curve. However, the authors construct Californian energy demand as composed by aggregate data, namely without differentiating among the heterogeneous climate zones. In this way, they might have omitted possible nonlinearities, generating biased estimates of future electricity demand.
The same issue occurs in De Cian et al. (2007)[31] contribution. They
10In general, for each additional day exceeding 32◦C, they predict a rise in annual
mortality in a range between 0.5% and 1.7% by the end of 21st century.
11The two scenarios are the A1 and the B2 projections, developped in 2000 IPCC SRES
provide a study12 where the impact of climate change is examined on an ag-gregated among thirty-one countries energy demand. They investigate two effects: the cooling effect, namely the increase in energy consumption (espe-cially, electricity) for cooling, and the heating effect, namely the reduction in fuel consumption (especially, gas and oil products) for heating. They find that cooling and heating effects vary across regions. In cold countries, where there is higher demand for heating, a negative correlation between general energy demand and temperature results. In mild countries, summer cooling effect balances winter and autumn heating effect. In warm countries, tem-perature has a positive correlation with energy demand during summer and spring.
De Cian and Sue Wing (2017)[32] overcome the nonlinearities issues. They introduce a methodology to study the geographical changes on energy de-mand due to climate change worldwide. Differently from most papers, for
climate variables, they use temperatures and humidity bins13. Their new
ap-proach consists of exposing energy demand to changes of the temperature and humidity bins, estimating short-run and long-run elasticities of (per capita) energy demand. Long-run responses are fundamental to absorb the
nonlin-earities of these responses14. Evaluating future impacts on energy demand by
205015, the two authors suggest that climate change has an additional role
on generating socioeconomic dynamics. Climate change might determine a redistribution of energy demand in two directions, from milder countries to tropic developping countries and from high-income group to low-income group. Change in climatic conditions might generate more inequalities, since it increases the share of disposable income allocated to energy consumption. The lower is the income, the higher will be the share.
Burke et al. (2009)[19] investigate whether climate change effect on
tem-12They conduct an error correction model (ECM) on a panel dataset about energy
demand for residential sector between 1978 and 2000. The ECM specification allows distinguishing between the short-run and the long-run adjustments.
13They collect 14 bins for temperature and 10 for humidity, constructed through daily
averages of Global Land Data Assimilation System (GLDAS) 0.25◦grid data of 3-hour surface temperatures and specific humidity. On the energy demand side, instead, they use ENERDATA database.
14The intensity and sign of the effects of different climatic conditions depend on
dif-ferent factors, such as the fuel (electricity, oil and natural gas) or the sector (domestic, agriculture, commercial, transportation and industry) or the geographic area taken into account.
15Combining previous estimates with climate change, demographic and economic
sce-narios, they find a rise in global energy use between 7% and 17%. This increase will be mainly driven by demand for cooling as response to warmer temperatures and higher humidity.
perature and precipitation may induce to more civil wars in Africa. They find that civil wars incidences are strongly determined by temperature. An
increase of just 1◦C determines a growth of 4.5% and of 0.9% in conflicts
respectively in the same year and in the next year.
Finally, Jacob et al. (2007)[58] examine the short-run impacts of unexpected weather shocks on criminal activities. Their findings indicate that crime is not an exogenous variable. In a week, warmer temperatures increase crimi-nal events’ incidence. Higher precipitations decrease violent crimes, but not property crimes.
2.2
Climate change impact on the adoption of air
con-ditioners
Rapson (2014)[82] says that ”air conditioners are the most-intensive home appliance in US and a major contributor to residential sector carbon emis-sions”. Even though the increasing demand for space cooling through air conditioning is clearly a crucial topic for the future, the debate is still not sufficiently developped (IEA, 2018[52]). In the climate-economy literature the impact of climate change on the decision of adopting air conditioning has not been sufficiently explored. Surveying the literature, Auffhammer and Mansur (2014)[10] write that ”[t]he literature examining the adoption of air conditioners in response to changes in climate is essentially non-existent.”. Thanks to the richness of data on air conditioning adoption and usage, most peer-reviewed works analyse the issue in the USA. Over the period 1960-1980 Biddle (2008)[15] analyses the diffusion of air conditioning in US from
commercial buildings (e.g. theaters and stores) to residential buildings16.
The author suggests that this boom in central (but also room) air condition-ers sales for residential buildings was mainly due to increases in real incomes, declines in electricity rates and decreases in the installation costs for these appliances. Other determinants are demographics (education) and housing characteristics (when the house was build, average number of rooms per res-idence). In the analysis, the author also includes climatic variables, namely HDD, CDD, wind speed and humidity. Nevertheless, even if he suggests the relevance of weather/climate in AC diffusion, the climatic variables are only
16In early 1950s almost 2% of US household had an air conditioner. Air conditioners were
considered as luxury goods, installed in commercial buildings. In 1960s thanks to changes in home construction and the application of central air conditioning, air conditioners sales boosted. According to Census of Housing, in 1960 occupied housing units with some form of air conditioning reached 12.6%. They, then, passed from 35.8% in 1970 to 58.5% in 1980.
used to capture the effects of the geographic differences (hot/cold seasons) on the AC penetration rate for different locations. The author’s interpretation does not take account of the role and, hence, of the consequences of changes in climate.
Remaining in the literature on USA, Sailor and Pavlova (2003)[84] study the relation between air conditioning market saturation and cooling degree
days17(CDD). Fitting their data18 they find that most analysed cities do not
presented a saturated market. There is still space for growth in the market. They also estimate the long-term market saturation response to CDD rises
due to climate change19. Their findings evidence that long-term AC market
saturation adjustments (extensive margin) might affect AC energy demand response to climate change more than short-run sensitivity responses (inten-sive margin). Nevertheless, as Auffhammer and Mansur (2014)[10] highlight, there are no other economic (e.g. income) and climate (e.g. precipitation) variables in Sailor and Pavlova (2003)[84] analysis. Moreover, their contri-bution mainly consists of a curve fitting analysis. It does not provide any evidence about the determinants of the air conditioning long-term diffusion. Rapson (2014)[82] estimates a dynamic, infinite-horizon, discrete-choice op-timisation model of energy durable goods demand, namely air conditioners (room and central units). As Sailor and Pavlova (2003), the space cooling needs are measured through cooling degree days (CDD). On the intensive margin side, electricity prices influence the magnitude of air conditioning use. On the extensive margin side, unit efficiency, more than unit price, affect household choice of installing or replacing an air conditioner. When households have to choose whether to purchase an air conditioner, they are more influenced by the possibility of increasing their savings thanks to unit efficiency improvements. Electricity prices, instead, are taken account as an interim variable.
Cohen et al. (2017)[27] study the cost of adapting to temperature increases.
They examine adaptation in both energy use and capital (AC20 and heating
17They use panel penetration data from 39 US cities across the years 1994-1996. Cooling
and heating degree days are calculated using hourly temperature data for a 10-15 year period of record from the National Climatic Data Center (NCDC).
18Sailor and Pavlova (2003)[84] fit data using the following AC market saturation curve
as function of CDD: S0 = 0.944 − 1.17e(−0.00298CDD) where S0 is the share of air
con-ditioning ownership or saturation rate. They analyse AC market saturation since in the long-run climate change might lead to AC demand exacerbation (extensive margin).
19Sailor and Pavlova (2003)[84] long-run AC saturation curve: S
f uture = S0 +
0.00349e(−0.00298CDD)∆CDD
20However, the decision of adopting air conditioning is not the target of their analysis.
They are, instead, interested to the cost for adapting residences to a warmer climate, also through air conditioning installation.
fuels) for residential sector. Their best finding21is the estimate of the present discounted value of the cost for adapting homes to the 2000 IPCC A2 ”high emissions” scenario (see IPCC, 2000[53]), averagely $5,600 per household.
These estimates change across US regions22.
Moving to literature on developing countries, air conditioning is a criti-cal task in these countries since it is more desirable where is less affordable (McNeil and Letschert, 2008[71]).
McNeil and Letschert (2010)[72] suggest that in these countries primary en-ergy consumption is characterised by an ongoing and steady growth, mainly driven by refrigerators and washing machines adoption. However, the au-thors forecast that air conditioning use in warmer developing countries will rise, not only more than the other appliances use, but also more than the national economic growths.
In a previous study, McNeil and Letschert (2008)[71], the two authors study both the future air conditioning energy use and the hidden potential of en-ergy efficiency in residential sector of developing countries. They predict the
AC diffusion curves23, as function of CDD and income, for many developing
countries or regions, such as Brazil, Mexico, the rest of Latin America, In-dia, Indonesia and so on. Estimates show that developing countries with the highest current rate of saturation will experience the highest growth in the widespread use of air conditioners. Based on these estimates, McNeil and Letschert (2008)[71] also study the importance of efficiency improvements to mitigate the consequences of such an increase in air conditioning adoption in developing countries. Improvements in efficiency result in ongoing reduc-tions in total energy use and, hence, in GHG emissions. These savings are achievable since developed countries have already reached them. However, variables, such as energy consumption and saturation from air conditioning, are more volatile and difficult to predict. They strongly depend on income. Therefore, how in developing countries middle class will grow might be cru-cial (McNeil and Letschert, 2008[71]). As Sailor and Pavlova (2003)[84], this
21Nevertheless, it is weakly significant (only at 10% level of significance).
22There is a strong difference between North regions and South regions. South regions
will suffer more for temperature increases. Their demand for milder indoor temperatures will be higher. Consequently, their cost of adaptation results equal to $29000 at the end of the century.
23They estimate diffusion curve of air conditioning demand in US using both the same
functional form and the same 39 cities of Sailor and Pavlova (2003)[84]. However, they calculate cooling degree days (CDD) through daily average temperatures, and not hourly data as in Sailor and Pavlova (2003)[84]. The estimated AC diffusion curve is the fol-lowing Saturation = 1.00 − 0.949e(−0.00187CDD). Substituting US data, they set this AC
work does not provide any explanation about the determinants of the future residential air conditioning diffusion in developing countries. Instead, it is mainly based on a data interpolation method.
Auffhammer (2014)[9] studies the extensive margin adjustments in China for air conditioning adoption due to climate change. The author shows that in
the short-run air conditioning adoption is sensitive to weather and income24.
Moreover, Chinese households’ response to climate change does not only con-sist of higher energy use from air conditioning, but also in a short-run increase in the use of existing air conditioner adoption.
Akpinar-Ferrand and Singh (2010)[5] focus on air conditioning adoption in India. The methodology they apply is based on Sailor and Pavlova (2003)[84] and on McNeil and Letschert (2008)[71]. Therefore, studying the impact of unexpected weather events in India, namely heat waves, they model air condi-tioning adoption as function of income and cooling degree days. Their results highlight the need of air conditioning adoption during heat waves. However, they also recommend an improvement in Indian energy policy. Since the cli-mate change will lead to an increase in average temperature, Indian demand for air conditioning will increase, raising at the same time the emissions of
CO2 and other greenhouse gases (GHG) in the atmosphere.
Davis and Gertler (2015)[30] study the relation between temperature, in-come and air conditioning adoption in Mexico. On the extensive margin, the authors find that annual CDD and income are strong determinants of the decision of adopting air conditioning.
About literature on European countries, Auffhammer and Mansur (2014)[10] highlight that ”[I]n Europe, data on air conditioner usage and adoption are scarce and the literature we could gain access to is thin as a result ”. Since there is no much literature on cooling demand in Europe, new contributions are necessary to improve European Union energy policy choices. Moreover, as Auffhammer and Mansur (2014)[10] suggest, when we look at European countries, we necessarily have to deal with the ”degree of heterogeneity in weather, electricity prices and incomes”.
Based on the proposed literature review, we can conclude that the reviewed literature on air conditioning adoption preferred to focus on the role played by climate and income paying less attention to how these variables interact with other socio-economic household characteristics.
24The author first uses panel data between 1995 and 2009 of 29 Chinese provinces about
air conditioning penetration rate to estimate the AC saturation curve, taking account of income, price of both air conditioners and electricity as well. Then, he studies short-run weather sensitivity of air conditioning adoption.
2.3
Previous analyses of the OECD 2011 EPIC survey
This dissertation is not the first analysis that have been conducted on the OECD 2011 EPIC survey data.Kristr¨om and Krishnamurthy (2014)[64] study the demand for renewable
energy and energy efficiency. Their results suggest that membership in an en-vironmental organisation, education, disposable income (proxied by dwelling size) and age are the main determinants of households’ willingness to pay (WTP) for green energy. Investigating energy efficiency, the authors find a strong owner-effect, which influences the purchase of more energy-efficient devices, especially of windows and thermal insulation. However, there is the same probability for owner and tenant to have access to government grants. Finally, they find strongly significant the influence on electricity consumption
of disposable income and membership in an environmental organisation25.
Ameli and Brandt (2015)[4] propose an analysis of the determinants of the choice of investing in renewable energy and energy efficiency. Their method consists of an econometric analysis through binary logit models. Then, they determine the best model specification using the Bayesian model averaging (BMA). Their estimates highlight that home owners, high-level income and environmentally-friendly household are more likely to invest in energy effi-cient and renewable energy.
Dato (2017)[29] recently analyses the interactions between investment deci-sions on energy efficiency and on renewable energy. The findings demonstrate that the two decisions have a positive synergy and cannot be analysed
in-dependently. Through a bivariate26 probit regression, Dato (2017)[29] finds
that civic engaged, environmentally aware, wealthier and non-tenant respon-dents are more likely either to jointly invest in energy efficiency and renewable energy or, at least, to invest in one of them. A further interesting result is the energy poverty. Since the probability of one investment affects the other one, poorer households might be less inclined to make investments in energy efficiency. Consequently, they spend a higher share of their income in elec-tricity.
Krishnamurthy and Kristr¨om (2015)[63] propose an in-depth investigation
on the electricity demand. Using the household annual electricity
consump-25Nevertheless, there is a main issue in their last analysis, which is determined by the
electricity demand equation they use. In fact, they include in the electricity demand the decision of purchasing an energy-efficient appliance, namely Ai. This variable may be
endogenous. As a consequence, all the coefficients they find might be biased.
26Since there is synergy between the two decisions, two univariate regressions might
tions indicated in 2011 EPIC survey, they derive the average electricity price. They also estimate the country-specific price and average income elasticities for the 11 OECD countries included in the survey. The average elastici-ties result to be low, rarely significant and without statistically significant cross-country variation. This might be due to the heterogeneity of elasticities among different countries, and inside larger countries as well.
Brown (2014)[18] aims to verify the relation among environmental attitudes
and civic engagement27. On the one hand, the results surprisingly suggest
that 2011 EPIC survey ”environmental sceptics” are more likely to be en-gaged in some civic organisations. On the other hand, they confirm that ”environmentally motivated” respondents are more likely involved in envi-ronmental organisations activities. Finally, applying these findings Brown (2014)[18] demonstrates that environmental incentive-based policies for wa-ter, waste and energy are mediated by environmental attitudes.
Even if the existing analyses have not included climate variables, they are useful to gather information about the households’ characteristics of the 2011 EPIC survey.
This dissertation contributes to the above-mentioned literature by address-ing five major gaps that have been identified. First, it aims to increase the empirical studies on the new climate-economy literature using as propaga-tion channel the energy end-use consumppropaga-tion for air condipropaga-tioning. Despite most prior peer-reviewed contributions, instead of studying climate change
impacts through weather shocks (e.g. Deschˆenes and Greenstone, 2007[35];
De Cian et al., 2007[31]; De Cian and Sue Wing (2017)[32]), this work is directly oriented to the households’ long-run adaptation to the phenomenon, using cross-sectional data in the analysis. As Sailor and Pavlova (2003)[84] suggest, long-run adjustments might have a stronger impact than short-run ones on the increase in both air conditioning energy use and market satu-ration. Hence, long-run responses will have role in future GHG emissions. Since the 2011 OECD EPIC survey data already reflect households’ long-run expectations, I am able to internalise adaptation processes in the analysis. My estimates are more likely to mimic the effect of a permanent shift in long-run climate with estimates from an equal permanent long-run change in climate.
27 First, to verify whether participants’ attitudes are influenced by those ones of their
neighborhoods, the author conducts a spatial analysis using the geocoded observations. This influence works as a weight. Then, after having divided respondents in three attitu-dinal classes, namely ”environmentally motivated”, ”technological optimists” and ”envi-ronmental sceptics”, Brown (2014)[18] runs a probit regression.
Second, this dissertation points to reduce the gap in the literature on the de-terminants of the choice of adopting air conditioning technologies. Most pre-vious papers, such as Sailor and Pavlova (2003)[84] and McNeil and Letschert (2008)[71], focus on predicting how households will adapt, in terms of air con-ditioning ownership, to change in climate conditions. However, they do not capture in their analyses which factors will determine a greater diffusion of residential air conditioning and how the influence of those factors might vary across countries and income levels.
Third, another gap that this work aims to reduce is about the literature on Europe. Indeed, most of the 11 countries involved in the 2011 OECD EPIC survey are European countries (France, Netherlands, Spain, Sweden, Switzerland).
Fourth, the focus of this work is on the extensive margin for the adoption of air conditioning. Sailor and Pavlova (2003)[84] suggest that to study the future energy demand for air conditioning, it is necessary to take account of the adjustments in air conditioning diffusion.
Fifth, even if it has not been reviewed, the scarce literature on thermal insula-tion adopinsula-tion focus on extensive margin responses to dwelling characteristics
and other socio-economic variables (Gillingham et al.m 2012[44]; Kristr¨om
and Krishnamurthy, 2014[64]; Ameli and Brandt, 2015[4]). To my knowledge, there are no contributions which study the technology adoption as response to changes in climate.
3
Thermal comfort and climate: a new
em-pirical analysis
This section presents data and methodology (3.1) providing also descriptive statistics for the variables of interest (3.2). The empirical framework used for the analyses is also provided (3.3). It corresponds to a discrete choice model on thermal comfort investment decisions for two technologies, namely air conditioning and thermal insulated wall and/or roof. Inspired by the previous analyses exploited in the literature, a set of empirical hypotheses is then drawn up (3.4).
3.1
Data and methodology
The dataset used for the analysis combines the Global Land Data Assimila-tion System (GLDAS) climate data with the 2011 OECD EPIC survey. This section describes the climate dataset obtained from the GLDAS and used for the analysis (3.1.1) and how it is merged with the 2011 OECD EPIC survey (3.1.2).
3.1.1 The Global Land Data Assimilation System (GLDAS)
The Global Land Data Assimilation System (GLDAS, see Rodell et al., 2004[83]) is a global reanalysis dataset which provides in near-time (48h),
high-quality results (0.25◦ x 0.25◦ resolution) combining satellite- and
ground-based observational data products, using data assimilation techniques and four advanced land surface models which are able to simulate optimal fields of water and energy states (e.g. temperature) and fluxes (e.g. evaporation) (Rodell et al., 2004[83]); Fang et al., 2009[42]).
For my analysis, to study the impact of climate change on thermal comfort’s decisions I opt for using long-term average annual CDD and HDD as a mea-sure of the typical intensity and duration of hot and cold climate. Heating and cooling degree days have been calculated using the daily temperature
data computed from the 3-hourly global surface gridded temperature (0.25◦x
0.25◦ resolution, approximately 27-28 km) fields obtained from the GLDAS,
for the years 1986-2011. Since the EPIC survey has been conducted in 2011, the explanatory variable to be used in the regression analysis is the long-term average of HDD and CDD over the period 1986-2011. This is in line with the practice of defining climatic conditions as the averages over a sufficiently long period of time (usually about 30 years, see Glossary in IPCC, 2014[56]). There are some reasons behind the adoption of cooling and heating degree days in this analysis. This methodology resulted to be proper for expressing
the relationship between climate and energy demand (Atalla et al 2018[7]). As we have also seen in Section 2.2., in the new climate-economy literature most scholars use cooling degree days to characterise the relationship between climate and air conditioning demand (Santamouris, 2016[85]). Moreover, also engineering authors suggest that there is a linear relationship between short-term changes in cooling degree days and energy demand for residential buildings (Biddle, 2008[15]), including air conditioning as well.
3.1.2 Combining climate gridded data and the OECD 2011 EPIC
survey
Given that most of the households in the EPIC survey are geocoded28 I use
latitude and longitude information to merge the household survey with the weather data. After having obtained the GLDAS 3-hourly global surface gridded temperature fields for the EPIC countries over the period 1986-2011, their daily temperature are computed in each grid-cell over the same pe-riod. Then, for each grid-cell the CDD/HDD are calculated using the general
method29 and fixing 18.3 ◦C as temperature baseline30. The general method
develops as follows : CDD = Nd X d=1 (γd)(T − Tb) HDD = Nd X d=1 (1 − γd)(Tb− T )
where Nd is the number of days in a specific month or year; T is the mean
daily temperature; γd is the binary multiplier (if T > Tb then γd = 1, 0
otherwise); Tb is the temperature threshold (18.3 ◦C).
Using the rgdal package I read in R the high-resolution spatial database of country administrative areas collected in the DIVA-GIS shapefile provided by the Database of Global Administrative Areas (GDAM). Since in the EPIC survey for each household we also observe the country and the region where
28All non-geocoded households are dropped, moving from 12202 to 7449 observations.
Unfortunately, this means to drop 3 out of 11 countries available in the dataset, namely Chile, Korea and Israel.
29To compute CDD/HDD some authors use the general method (e.g. Sailor and
Pavlova, 2003[84]), some others use different methods. For instance, McNeil and Letschert (2008)[71] use Erbs et al. (1983)[40] computation.
3018.3 ◦C is the most used temperature threshold in the literature (e.g. Sailor and
Pavlova, 2003[84]; Aebischer et al., 2007[1]; Akpinar-Ferrand and Singh, 2010[5]; Deschˆenes and Greenstone, 2011[36]; Rapson, 2014[82]; Cohen et al., 2017[27])
it lives, I renamed the regions collected in the EPIC survey dataset accord-ing to the shapefile. Then, this shapefile is used to transform households’ latitude and longitude in spatial points running the SpatialPoints function. The subsequent step consists of reading in R both the 2011 OECD EPIC sur-vey and the gridded CDD/HDD datasets computed using the temperature fields from GLDAS and to use the extract function of the raster package to extract GLDAS CDD/HDD observations for each OECD participant through geo-location. More in details, the HDD/CDD grid-cells (boxes) are retained only where the OECD spatial points fall over them. In this way, a grid-cell is assigned to each household. Then, each grid cell, and therefore each house-hold as well, is assigned to the region that overlaps with the largest share of the grid-cell.
Figure 1 shows a exemplification of what occurs during the merge. The grid-cell shown in orange is assigned to Region 2. Thus, the household is assigned to Region 2 as well. I checked that all the households were assigned to their correct regions.
Note that the GLDAS grid-cells close to water have missing values. These households are dropped remaining with 6821 households. Moreover, for the merge I also take account of their survey ID, rather than the geocode, since some households are very near each other, namely they share the same lati-tude and longilati-tude. Once I have the CDD/HDD between 1986-2011 for each household, the long-term mean values are computed. Therefore, the merge’s outcome is two new datasets, one with HDD and one with CDD, which con-sist of the initial 2011 OECD EPIC survey dataset plus the observed long term average cooling/heating degree days over the period 1986-2011. Finally, CDD and HDD datasets are then merged in STATA.
Figure 1: Exemplification of the data merging
As the 2011 OECD EPIC survey was built using the quota sampling method, after the merge, I check the quota targets for the full-sample and for the country-samples. The rates are most of the time respected. Therefore, I do not need to set weights in my analysis, because the sample representativity is confirmed.
In Appendix, Figures from 20 to 27 display CDD and HDD maps for the eight EPIC countries included in the analysis. Each black point represents a geocoded household.
3.1.3 The wealth index
In my analysis, given the important role of income as driver of air condi-tioning adoption, in addition to using the annual income variable available
from the dataset (e.g. Ameli and Brandt, 2015[4]; Kristr¨om and
Krishna-murthy, 2014[64]; Krishnamurthy and Kristr¨om, 2015[63]; Dato, 2017[29]),
following Filmer and Pritchett (2001)[43] I also use an alternative measure, the so-called wealth index. A first reason to construct the wealth index is for income the full sample is not available. For the income question, in fact, there is the option of declaring either ”I do not know” or ”I prefer not to answer”. About 1000 participants answered one of them. Thus, I would lose about 15% of the full sample, reducing its statistical power. The income vari-able is also not necessarily relivari-able. There might be some participants that might have voluntarily underestimated their annual income. Indeed, income is sensible information and, therefore, households might have incentives to misreport it. This issue is called measurement error. Moreover, the wealth index may capture different effects in the analysis. Indeed, annual income is
subject to short-run shocks (e.g. a household head might lose its job during the year). Wealth index is a more stable variable which better captures the long-term situation of a household.
The wealth index I propose is an asset-based index which measures the-socio economic status (SES) of each participant. The main advantage of this measure is related to its capability of capturing long-run household’s wealth (Vyas and Kumaranayake, 2006[91]). To create this index, I apply the principal component analysis (PCA), also called proper orthogonal de-composition (POD). The PCA can be defined as a multivariate statistical simplification data method. It provides uncorrelated factors or components through a linear weighted transformation which projects in a Cartesian co-ordinate system the initial correlated variables, starting from that one with the higher variance. The aim is to maximise the variables’ variance along the axes in the new coordinate system. Hence, the components are ordered such that the first one explains the most of the variation in the initial data. Each subsequent component is uncorrelated and explain a new dimension of the data, gradually diminishing its explanatory power about the variations in the initial data. Mathematically, the PCA can be described as follows (Vyas and Kumaranayake, 2006[91]):
C1 = ω11X1+ ω12X2+ ... + ω1nXn
.. .
Cm = ωm1X1+ ωm2X2+ ... + ωmnXn
where X is the set of the n initial variables, C is the set of the m components and ω is the set of the weight for the n-th variable in the m-th component. The assets which have the higher variance (unequally distributed among households) weight more in the PCA.
This multivariate statistical tool is practically useful to create a wealth in-dex whether the assets’ distribution varies across households, as in the EPIC dataset (Vyas and Kumaranayake, 2006[91]). The major issue related to the usage of a wealth index through PCA are households’ clustering (clusters of distinct households) and truncation (households distributed in a narrow range). They make difficult to create SES classes for the analysis. The prob-lem can be solved either adding more assets or adding continuous variables or adding high variance assets (Vyas and Kumaranayake, 2006[91]).
To build a wealth index literature does not define a minimum number of assets that must be included. In previous contributions the number of as-sets used varies between 10 and 30 (Vyas and Kumaranayake, 2006[91]). I choose 17 variables available in 2011 OECD EPIC survey (see Table 1). I
