The Greenness of Cities: Carbon Dioxide Emissions and Urban Development

Transcription

1 The Greenness of Ctes: Carbon Doxde Emssons and Urban Development By Edward L. Glaeser* Harvard Unversty and NBER and Matthew E. Kahn UCLA and NBER WP Abstract Carbon doxde emssons may create sgnfcant socal harm because of global warmng, yet Amercan urban development tends to be n low densty areas wth very hot summers. In ths paper, we attempt to quantfy the carbon doxde emssons assocated wth new constructon n dfferent locatons across the country. We look at emssons from drvng, publc transt, home heatng, and household electrcty usage. We fnd that the lowest emssons areas are generally n Calforna and that the hghest emssons areas are n Texas and Oklahoma. There s a strong negatve assocaton between emssons and land use regulatons. By restrctng new development, the cleanest areas of the country would seem to be pushng new development towards places wth hgher emssons. Ctes generally have sgnfcantly lower emssons than suburban areas, and the cty-suburb gap s partcularly large n older areas, lke New York. *Glaeser thanks the Taubman Center for State and Local Government, The Rappaport Insttute for Greater Boston, and the Manhattan Insttute. Kahn thanks the Rchard S. Zman Center for Real Estate at UCLA. Krstna Tobo and Ryan Vaughn provded excellent research assstance.

2 Introducton Whle there remans consderable debate about the expected costs of global warmng, a growng scentfc consensus beleves that greenhouse gas emssons create sgnfcant rsks of clmate change. A wde range of experts have advocated reducng ndvdual carbon footprnts and nvestng bllons to reduce the rsks of a major change n the earth s envronment (Stern, 2008). Almost 40 percent of total U.S. carbon doxde emssons are assocated wth resdences and cars, so changng patterns of urban development and transportaton can sgnfcantly mpact emssons. 2 How do major ctes dffer wth respect to ther per-household emssons levels? In Secton II of ths paper, we revew the basc theory of spatal envronmental externaltes. If emssons are taxed approprately, then prvate ndvduals wll make approprate decsons about locaton choces wthout any addtonal locaton-specfc polces. When emssons are not taxed, then locaton decsons wll be neffcent. The optmal locaton-specfc tax on buldng n one place versus another equals the dfference n emssons tmes the gap between the socal cost of emssons and the current tax on these emssons. Even f there was an approprate carbon tax, locaton decsons mght stll be sub-optmal f governments subsdze development n hgh emssons areas or artfcally restrct development n low emssons areas. In Secton III of ths paper, we measure household carbon doxde emssons producton n 66 major metropoltan areas wthn the Unted States. 3 For a standardzed household, we predct ths household s resdental emssons and emssons from transportaton use. We look at emssons assocated wth gasolne consumpton, publc transportaton, home heatng (fuel ol and natural gas) and electrcty usage. We use data from the 200 Natonal Household Travel Survey to measure gasolne consumpton. We use year 2000 household level data from the See also the crtcal revews n Wetzman (2007) and Nordhaus (2007). 2 See for sources of carbon doxde emssons. 3 Our work parallels the fndngs of the Vulcan Project at Purdue Unversty ( and the recent Brookngs Insttuton study by Brown and Logan (2008) fall nto ths category. Our exercse s slghtly dfferent snce we look at the mpact of a standardzed household and we focus on margnal, rather than average, homes. For an example of nternatonal analyss that dsaggregates greenhouse gas emssons varaton wthn a naton, see Auffhammer and Carson s (2008) study of Chna. 2

3 Census of Populaton and Housng to measure household electrcty, natural gas and fuel ol consumpton. To aggregate gasolne, fuel ol and natural gas nto a sngle carbon doxde emssons ndex, we use converson factors. To determne the carbon doxde mpact of electrcty consumpton n dfferent major ctes, we use regonal average power plant emssons factors, whch reflect the fact that some regons power s generated by drter fuels such as coal whle other regons rely more on renewable energy sources. We dstngush between the emssons of an area s average house and the emssons of a margnal house by lookng partcularly at homes bult n the last twenty years. Our estmates suggest a range of carbon doxde emssons from about 9 tons per household per year n San Dego and Los Angeles to about 32 tons n Oklahoma Cty and Memphs. The older ctes of the Northeast tend to le wthn those extremes. Whle people n these older ctes drve less, they need large amounts of heatng and produce more emssons as a result. For llustratve purposes, we use a socal cost fgure of 43 dollars per ton of carbon doxde, whch mples that the socal cost of a new home n Houston s $550 dollars more per year than the socal cost of a new home n San Francsco. We also use our methodology to compare the emssons n central ctes and suburbs for 48 major metropoltan areas. In general, central cty resdence s assocated wth lower levels of emssons, although there are a few places where that fact s reversed. Carbon doxde emssons dfferences wthn metropoltan areas are smaller than the dfferences across metropoltan areas. The place wth the most extreme emssons dfference between central ctes and suburbs s New York, where we estmate that suburban development causes more than 300 dollars more damage n carbon doxde emssons than central cty development. Across metropoltan areas, we fnd a weak postve connecton between the level of emssons and recent growth when we weght by ntal populaton sze. We fnd a strong negatve correlaton between emssons and the level of land use controls. Overall, the metro areas wth the lowest per-household carbon doxde emssons levels are also the most restrctve towards new development. Ths fact suggests that current land use restrctons may be dong exactly the opposte of what a clmate change actvst may have hoped. Those restrctons, often mplemented for local envronmental reasons (such as to preserve open space or reduce neghborhood traffc), seem to push new development towards the least envronmentally frendly 3

4 urban areas (Fschel 999, Glaeser and Tobo, 2007). We now turn to the basc economcs of envronmental externaltes and urban development. I. Urban Development and Envronmental Externaltes Ths theory secton makes three smple ponts. Frst, f emssons are actually taxed at the approprate rate then there s no need for further spatal polcy to mprove prvate decsons about locaton. Second, f emssons are taxed below the optmal level, then t s approprate to subsdze the areas that have less energy usage and tax the areas wth more energy usage. Thrd, even wth an optmal emssons tax, suboptmal publc polces, such as zonng or transport subsdes, may stll lead to suboptmal locatons. We outlne a smple model where locaton choce nteracts wth envronmental externaltes. We assume that there s a fxed populaton of sze N dentcal ndvduals that must choose between I communtes. The populaton of communty s denoted N. Indvduals choose ther communtes and ther level of energy consumpton. We let E denote the amount of energy selected by each ndvdual. Ths energy choce s meant to nclude household and transportaton-related energy use. H E Indvduals maxmze Y P ( P + t) E + teˆ + V ( E; Z ) C( NEˆ ), a quas-lnear utlty functon where Y refers to ncome, H P refers to housng costs that are specfc to regon, refers to energy costs whch are specfc to regon, t refers to an energy tax whch s ntally ndependent of regon, Ê refers to the average energy consumpton n the world as a whole, refers to attrbutes of the area whch ndvduals treat as exogenous, V(.;.) reflects the regonspecfc benefts from energy use and C ( NEˆ ) represents the costs of global energy consumpton that may be assocated wth clmate change. Each ndvdual pays an energy tax of te, but then receves a tax refund of te ˆ so that the tax s revenue neutral. The functon V(.;.) allows dfferent area characterstcs to nfluence the benefts from energy use, and s meant to capture the E P Z 4

5 possblty that ar condtonng mght be more valuable n hot, humd places. We assume that V(.;.) s ncreasng and concave n ts frst argument. Income and housng costs are derved from labor markets and housng markets. Specfcally, each regon has F Q dentcal employers who earn revenues, denoted f(.), that are ncreasng and concave n the number of people hred. Each regon also has B Q of bulders whose costs, denoted k(.), are ncreasng and convex n the number of buldngs produced. The employers and bulders are owned equally by all of the people n the country. These assumptons enable us to wrte that wage ncome equals N f ' Q F, the margnal product of labor, and the cost of housng N equals k' B, the margnal cost of supplyng housng. Each person receves an equal share of Q all busness profts throughout the world. Equlbrum s then determned by two optmalty condtons. Frst, ndvduals must be choosng ther prvate energy consumpton to maxmze ther utlty levels whch mples that E * P + t = V ( E ; Z ), where * E denotes prvately optmal energy consumpton condtonal on * prces and taxes n area, and V ( E ; Z ) s the dervatve of V(.;.) wth respect to ts frst argument. Indvduals must also be ndfferent between the dfferent locatons, whch means that N N E * f ' k' ( t + P ) E + V ( E F B Q Q * ; Z ) must be constant across space. Snce everyone s essentally dentcal, we focus on an addtve socal welfare functon: F N B N E () Q f Q k + N ( V ( E ; Z ) P E C( NEˆ) ) F B Q Q. E Ths yelds frst order condton for energy consumpton: P + NC ( NEˆ) = V ( E ; Z ), whch ' gves the standard result that the prvate optmalty condton wll be equvalent to the socal optmalty condton f t = NC' ( NEˆ ). The frst order condton for socal optmalty locatons s 5

6 N N E that f ' k' + V ( E ; Z ) E ( P + NC' ( NEˆ) ) Q F Q B s constant across space. Ths condton s satsfed f t = NC' ( NEˆ ). There s no need for any added spatal polces f energy s properly taxed. If t NC' ( NEˆ ), then the spatal equlbrum s not Pareto optmal because people don t fully nternalze the externaltes assocated wth ther energy use when they change locatons. If energy use n an area s ndependent of the number of people n that area, then a locaton specfc * tax of E ( NC'( NEˆ) t) transforms the prvate locaton decson nto a second best socal optmum, where people make the socally optmal locaton decsons condtonal upon ther socally suboptmal energy decsons. In comparng any two areas, the dfference n tax payment * * for area versus area j should equal ( E )( NC'( NE t) E 2 ˆ), the dfference n energy usage tmes the dfference between the optmal tax and the current tax. Our prmary emprcal exercse wll be to calculate these quanttes for dfferent areas. We can use the same model to ask when local envronmentalsm s good envronmentalsm. We model local envronmentalsm by assumng that a locaton mposes a locaton specfc tax, τ on energy usage n that state, and that revenues from ths tax are rebated to the resdents of the state. The frst order condton for ndvdual energy consumpton s now P E ** ** + t +τ = V ( E ( τ ); Z ), where E ( τ ) s a functon mappng local energy taxes nto local ** energy use. The concavty of V(.;.) mples that E '( τ ) < 0. Hgher taxes wll lead to local energy decsons that are better from a global perspectve as long as t +τ NC' ( NEˆ < ), but they wll not necessarly ncrease welfare because these taxes also mpact mgraton decsons. To make ths pont, we reduce the world to only two regons and assume that there s no energy tax n regon 2. We further assume that t +τ NC' ( NEˆ ). Dfferentatng the spatal equlbrum yelds: 6

7 (2) N τ = Q B E N k" Q ** B ( τ ) + Q2 ** E ** ( V ( E ( τ ); Z ) ( t + P ) E ( τ )) B N k" Q 2 B 2 Q F N f " Q F F Q2 N f " Q 2! F 2 < 0, so a tax on energy use n regon one reduces the populaton of regon one. Ths effect mght be qute small, especally f the tax s modest, because the tax mpacts mgraton behavor only by nducng people n area one to consume too lttle energy relatve to the prvately optmal level of energy consumpton n the absence of ths tax. The tax n regon one mproves overall socal welfare f and only f: ˆ) NC'( NEˆ) t NC'( NE t τ ** ** (3) N E ( τ ) > ( E ( τ ) E ) 2 N τ ** The left hand sde of the equaton s postve; the rght hand sde s negatve f E ( τ ) > E2. If energy usage n regon one s greater than energy usage n regon two, then the mpact of added energy taxes n that regon must have a postve effect on welfare. In that case, the tax reduces both energy consumpton, and the number of people n regon one, whch s desrable snce t s the hgh energy use regon. If regon one s usng less energy than regon two, then the stuaton s more ambguous. If the mgraton margn s very large then t s at least concevable that ths tax wll make the energy problem more problematc. A local tax that sets t +τ = NC'( N ˆ) s certanly sub-optmal, E snce n that case the gans from reducng the tax on the mgraton margn wll exceed the costs of reducng the tax n terms of ncreased energy usage n regon one. In many cases, ths result may be more of an economc curosty than a real concern. Many energy taxes seem too small to really mpact mgraton behavor, at least f the taxes are rebated to resdents n some way. However, envronmentally nspred land use restrctons seem more lkely to have counterproductve results. To model these nterventons, we assume that locaton one has mposed a tax on new constructon equal to z whch s meant to refer to a zonng tax. Wth ths tax, the equlbrum frst order condton for bulders n locaton one 7

8 H N satsfes P = + z k'. We assume that the tax ether goes to nfra-margnal resdents of B Q the communty or that t s shared across both communtes. 4 Unlke the place-specfc energy tax, the zonng tax does not mpact energy use drectly, but t does reduce the number of people n locaton one. Specfcally: N (4) = < 0. z N N 2 N N 2 k" + k" f " f " B B B B F F F F Q Q Q2 Q2 Q Q Q2 Q2 The overall mpact of zonng on socal welfare s ( E E )( NC ( NE ˆ t) + z ) E NC NE) postve as long as ( 2 )( ) 2 ) N z, whch s E ( ˆ t > z. If the area wth the hgh zonng tax s also the hgh energy user, then the zonng tax wll mprove welfare, at least untl the pont where the tax equals the dfference n energy usage tmes the dfference between the socal cost of energy use and the current tax. If the zonng tax s mposed n areas that have partcularly low energy use, then t s counterproductve. Ths motvates our emprcal exercse examnng whether areas wth extensve land use restrctons are also areas that have hgh levels of energy use. II. Greenhouse Gas Emssons Across Metropoltan Areas We now turn to estmatng the quantty of carbon doxde emssons that households produce n 66 major metropoltan areas. 5 Our goal s to calculate the margnal mpact of an extra household n locaton j on the total carbon doxde emssons of that locaton. The margnal household and the average household need not be the same, and we wll try to create margnal estmates by comparng the emssons of an average household and the emssons assocated wth 4 If the tax s rebated only to new homeowners then the tax wll be completely rrelevant. 5 Our sample ncludes 66 metropoltan areas wth at least 250,000 households based on year 2000 Census IPUMS. In the year 2000, 72% of all metropoltan area resdents lve n one of these 66 metropoltan areas. We use the IPUMS defntons of metropoltan areas to assgn households to metropoltan areas (see Table Two lsts the set of metropoltan areas that we study. 8

9 more recent development. Ideally, we would also be able to address the possblty that margnal emssons assocated wth more electrcty generaton are dfferent from the average emssons, but we have no way of dong ths well. In prncple, the margnal resdent could foster the development of a new lower pollutng electrc power plant, or the margnal megawatt of electrcty could nvolve more harmful energy uses. 6 We consder four man sources of carbon doxde emssons: prvate wthn-cty transport, publc transportaton, resdental heatng (natural gas and fuel ol) and resdental electrcty consumpton. Car usage and home heatng nvolves a relatvely smple translaton from energy use to carbon doxde emssons. Household electrcty use and publc ral transt requres us to convert megawatt hours of usage nto carbon doxde emssons by usng nformaton about the carbon doxde emssons assocated wth electrcty producton n dfferent regons of the country. We are not consderng the mpact of shftng people on the energy emssons assocated wth movng goods and we are not consderng the mpact of shftng people on ndustral output. The problem of fgurng out how ndustral locaton and the transport network changes wth dfferent urban development patterns s beyond the scope of ths paper. 7 One natural concern wth our approach s that households n areas that spend more on energy have less ncome to spend on other thngs that also nvolve greenhouse gas emssons. If people n Texas are spendng a lot on ar condtonng and gas at the pump, then perhaps they are spendng less on other thngs that are equally envronmentally harmful. We cannot fully address ths concern, snce t would requre a complete energy accountng for every form of consumpton, but we do not beleve our omssons fatally compromse our emprcal exercse. After all, few forms of consumpton nvolve nearly as much energy use as the drect purchase and use of energy. Moreover, areas that tend to have hgh levels of energy use are generally low cost areas lke far flung suburbs or the Sunbelt, where people have more, not less, money avalable for other thngs. One can argue that the hgh land costs n expensve ctes represent a transfer to earler property owners who use ther property-related revenues to buy more energy, but tracng through ths chan of money and emssons s far too complcated a task for us. 6 To the extent that all regons have a smlar relatonshp between margnal and average usage, then the mplcatons of ths work for nter cty comparsons, may not be terrbly effected by our nablty to measure true margnal mpacts. 7 Snce much of modern ndustry s captal ntensve and has low transport costs, we suspect t mght not move that much n response to a populaton shft. 9

10 Car Usage and Emssons We begn wth estmatng gasolne usage across metropoltan areas. Our prmary data source s the 200 Natonal Household Transportaton Survey (NHTS). Ths data source contans nformaton on household characterstcs and reported annual mles drven. The NHTS uses nformaton on the types of vehcles the household owns to estmate annual gasolne consumpton. 8 The survey also reports the populaton densty of the household s census tract, and zp code dentfers that enable us to use zp code characterstcs to predct gasolne usage. We use these zp code dentfers to calculate each household s dstance to the metropoltan area s Central Busness Dstrct. Our prmary approach s to use the NHTS to predct gasolne usage based on ndvdual and zp code level characterstcs. We regress: where j q (5) Gasolne = β j Z k + γ q X + µ k + ε j j Z k refers to the value of zp code characterstc j n zp code k, those varables, q β j reflects the mpact of q X refers to the value of ndvdual level q for person, γ q s the coeffcent on that characterstc and the other two terms are ndvdual level and zp code level error terms. Snce there are a sgnfcant number of truly extraordnary outlers, and snce we are runnng ths regresson n levels rather than logs, we top code the top one percent of the sample. The results of ths equaton are shown n Table. The overall r-squared of the equaton s 30 percent. Famly sze and ncome strongly ncrease gas consumpton, so t s mportant to control for these characterstcs. The area-level characterstcs have the predcted sgns. Populaton densty, whether at the tract, zp code or metropoltan area level, reduces gasolne usage (see Golob and Brownstone 2008). Dstance to the metropoltan s central busness dstrct also ncreases average gasolne consumpton. We also 8 For an analyss of how urban form affects vehcle mles traveled based on the 990 verson of ths mcro data set Bento et. al. (2005). 0

11 nteract census tract densty wth regon dummes and fnd that the densty-gas consumpton relatonshp s weaker n the West. We then take these coeffcents and predct gasolne usage for a famly wth an ncome of 62,500 dollars and 2.62 members for each census tract located wthn 66 major metropoltan areas. 9 j β jz + k q j Specfcally, our predcted value for a census tract wth characterstcs γ X q q Ave, where j Z k s q X Ave denoted the ndvdual characterstcs of a standardzed ndvdual. We then form metropoltan area averages by aggregatng up from the tract level usng the tract s household count as the weght. 0 These estmates control for household level ncome and sze, but they are, of course, mprecse. We are only usng two prmary characterstcs for each tract, ts proxmty to downtown and ts populaton densty. As such, there wll be an almost automatc relatonshp between urban sprawl and gasolne usage snce gasolne usage decreases wth densty and ncreases wth dstance from downtown. There s a less automatc connecton between gasolne consumpton and metropoltan area populaton sze, whch s shown n Fgure. On average, a. log pont ncrease n MSA populaton sze s assocated wth a 7.3 gallon reducton n the consumpton of gallons of gas. An alternatve approach s to run regresson (5) usng metropoltan area fxed effects nstead of regon fxed effects, and then use those metropoltan area fxed effects as our measure of gasolne usage. In that case, we would have had to restrct our work to the small number of metropoltan areas wth reasonably large data samples. We have estmated metropoltan area gasolne usage n ths alternatve manner, and the correlaton between our measure and the measure estmated usng metropoltan area fxed effects s hgh. To estmate the gasolne related emssons of a margnal household, we agan start wth the gasolne consumpton predcted at the tract level usng our coeffcents shown n Table. We then aggregate census tract gasolne usage up to the metropoltan area, by averagng across census tracts, weghtng not by current populaton levels, but nstead by the amount of housng 9 These demographc statstcs are based on the sample means for the 66 metropoltan areas from the year 2000 Census IPUMS. 0 We nclude all census tracts wthn thrty mles of the metropoltan area s CBD.

12 bult between 980 and If the locaton of housng n the near future looks lke the locaton of housng n the near past, then the locaton of recent constructon gves us some dea about where new homes wll go. On average, homes bult n the last 20 years are assocated wth 47 more gallons of gasolne per household per year than average homes, whch reflects the tendency to buld on the urban edge. Whle we beleve that focusng on recent housng patterns adjustment makes sense, t makes lttle dfference to the cross-metropoltan area rankngs. The correlaton between estmated metropoltan area gasolne consumpton usng the total populaton of each census tract and the estmate based on the number of houses bult snce 980 s.96. To convert gallons of gasolne nto carbon doxde emssons, we multply frst by 9.564, whch s a standard factor used by the Department of Energy. Ths converson factor ncludes only the drect emssons from a gallon of gasolne, not the ndrect emssons assocated wth refnng and delverng gas to the pump, whch typcally ncrease the energy use assocated wth a gallon of gas by 20 percent. 2 To reflect ths, we assume that each gallon of gas s assocated wth pounds of carbon doxde emssons. Publc Transportaton We now turn to the emssons assocated wth publc transportaton. There are no adequate ndvdual surveys that can nform us about energy usage by bus and tran commuters. Instead, we turn to aggregate data for each of the naton s publc transt systems from the Natonal Transt Database 3. For all of the naton s publc transt systems, ths data source provdes us wth nformaton about energy used, whch takes the form of gasolne n the case of buses and electrcty n the case of ral. The data does not tell us about prvate forms of publc transt, such as prvate bus lnes or taxs or the Las Vegas monoral. For each bus or ral system, the data set provdes us wth the zp code of ther headquarters. We then assgn each zp code to the relevant metropoltan area and sum up all of the gasolne and See 2 A typcal energy effcency fgure for gasolne s 83 percent:

13 electrcty used by publc transt systems wthn each metropoltan area. Ths provdes us wth total energy usage by publc transt for each metropoltan area. To convert energy use nto carbon doxde emssons, we contnue to use a factor of for gasolne. We agan ncrease that factor by 20 percent to reflect the energy used n refnng and dstrbuton. The converson for electrcty s somewhat more dffcult, snce electrcty s assocated wth dfferent levels of emssons n dfferent regons of the country. We wll therefore be usng dfferent converson factors for electrcty n dfferent places, and we wll dscuss those at length when we get to home electrcty usage. By combng emssons from gas and emssons from electrcty, we estmate a total emssons fgure wthn the metropoltan area. To convert ths to a household-level fgure, we dvde by the number of households n the metropoltan area. There are two reasons why the margnal emssons from a new household mght not be the same as the average emssons for an exstng household. Frst, the margnal household mght be more or less nclned to use publc transportaton. Second, even f the margnal household uses publc transport, we do not know how much extra energy ths wll ental. Typcally, we thnk of some publc transt technologes as havng large fxed costs, whch could mean that the margnal costs are qute low. However, n some cases, new development may mean that a new bus lne s extended to a newer, lower densty area, and n ths case, the margnal costs mght be qute hgh. Snce we lack the data to make an effectve estmate of the margnal effect, we wll use the average emssons from publc transt throughout ths paper. Snce the emssons from prvate automobles are on average ffty tmes hgher than the emssons from drvng, the benefts to our overall estmates of mprovng the accuracy of our publc transt emssons measures are lkely to be small. Household Heatng We now turn to the emssons from the two prmary household heatng sources: fuel ol and natural gas. Fuel ol use s rare n the Unted States outsde of the Northeast, and s an mportant 3

14 source of home heatng n only a few metropoltan areas. Natural gas s the more common source of home heat. In some areas, electrcty also provdes heat, but we wll deal wth electrcty separately n the next secton. For our purposes we need a large representatve sample that provdes nformaton by metropoltan area on household heatng. The Department of Energy s Resdental Energy Consumpton Survey 4 s too small of a data set to address our needs. Ths data set also does not provde each survey respondent s metropoltan area. Instead, we use data from the 2000 Census fve percent sample (IPUMS). Ths data set provdes nformaton for each household on ts expendture on electrcty, natural gas and fuel ol. The key problem wth the IPUMS data s that we are nterested n household energy use, not energy spendng. Convenently, the Department of Energy provdes data on prces for natural gas 5 and fuel ol 6 for the year These prces are at the state level, so we mss varaton n prces wthn the state. We use these prces to convert household energy expendture to household energy consumpton. One partcular problem wth the expendture data s that some renters do not pay for energy drectly, but are charged mplctly through ther rents (Levnson and Nemann 2004). These renters wll report zero energy expendtures, when they are ndeed usng electrcty and some home heatng fuel. Indeed, when we look at the frequency of reported zero expendture n dfferent metropoltan areas, we fnd that these tend to be dsproportonate among renters and other resdents of mult-famly houses. In these cases, t s mpossble to know whether a zero value for expendture truly ndcates that the household does not consume ths partcular fuel or whether the household just doesn t pay drectly for that energy. As such, we have the most confdence n the IPUMS data for measurng actual household energy consumpton for owners of sngle famly homes. We use the IPUMS 2000 data to estmate a separate regresson for each of the 66 metropoltan areas usng the subsample of owners of sngle famly homes:

15 (6) Energy Use=a*Log(Income)+b*Household Sze +c*age of Head+ MSA Effects. In the case of natural gas n the New York Cty area, for example, we estmate: (6 ) Natural Gas = Log( Income) Sze +.8 Age. (2.5) (.2) Standard errors are n parentheses. In ths regresson, there are 28,757 sngle owner occuped housng unt observatons and the r-squared s.02. For each metropoltan area, we estmate smlar regressons for fuel ol and electrcty consumpton. We then use metropoltan area specfc regresson coeffcents to predct the natural gas and fuel ol consumpton for a household wth an ncome of 62,500 dollars and 2.62 members. We try to correct for ndvdual characterstcs, but we do not correct for housng characterstcs. After all, we are not attemptng to estmate emssons assumng that people n Houston lve n New York Cty apartment buldngs. The buldng szes n an area are a key component n emssons and we want to nclude that. Our approach allows for the fact that a household wth a fxed set of demographcs s lkely to lve n a larger, newer home f t lved n Houston than t would have chosen f t lved n Boston or New York Cty, snce land prces are hgher n the latter ctes. Our approach captures the fact that a standardzed household wll lve ts lfe dfferently dependng on the relatve prces that t faces n dfferent ctes. To estmate energy consumpton for renters and owners n multfamly unts for each of the 66 metropoltan areas, we adjust our metropoltan area specfc predctons that were based on estmates of equaton (6). For example, we wll estmate equaton (6) usng Census IPUMS data for Los Angeles owners of sngle famly homes. Ths yelds a predcton of average electrcty consumpton for Los Angeles home owners of sngle famly homes for a household wth standardzed demographcs. We stll need to mpute what ths household s electrcty consumpton would have been f t had lved n Los Angeles as a renter of a sngle famly home, an owner of a unt n a mult-famly unt, or as a renter n a mult-famly unt. To mpute these last three categores, we use a second mcro data set called the 200 Resdental Energy Consumpton Survey (RECS). 7 Ths data set s a natonal sample wth 4,392 households that (.5) (.02) 7 5

16 ncludes actual household energy consumpton data. We use ths energy consumpton data to estmate natonal level OLS regressons; of the form: (6 ) Log(Energy Consumpton) = Controls + b *Owner + b 2 *Mult-Famly + U Usng the OLS estmates of b and b 2 from these regressons, we adjust our metropoltan area specfc predctons of energy consumpton. For example, suppose that we estmate usng the natonal data that b 2 equals zero and that b equals.. If based on our Los Angeles regresson for owners of sngle famly homes, we predct that the average home owner (wth standardzed demographcs) consumes 9 megawatt/hours of electrcty per year, then we would mpute that the average renter consumes 8.8 megawatt/hours of electrcty per year. 8 Ths procedure allows us to predct a standardzed household s consumpton of energy for each metropoltan area, f t lved n four dfferent housng categores. We then calculate a weghted average across these four categores by metropoltan area. The weghts, whch vary by metropoltan area, are based on the IPUMS data s frequency count of each of these four housng types. Ths mult-step method allows us to mpute the energy consumpton for renters and all resdents n mult-famly buldngs, where we are concerned that reported energy expendture does not accurately measure household consumpton. Our correcton procedure s especally mportant n a metropoltan area such as New York Cty, and s much less mportant n places lke Houston where most of the households are sngle famly owners. Natural gas consumpton s drven prmarly by clmate. Fgure 2 shows the correlaton between our estmated natural gas consumpton and January temperature. We do not fnd the correlaton coeffcent of -.8surprsng, but t does suggest that our results are reasonable. For fuel ol and natural gas, there are agan converson factors that enable us to move from energy use to carbon doxde emssons. In the case of fuel ol the factor s pounds of carbon doxde per gallon of fuel ol 9. We agan ncrease ths number by 20 percent to reflect the energy used n refnng and dstrbutng. Accordng to the same source, there are pounds of carbon doxde emssons per,000 cubc feet of natural gas. In ths case, there s much less energy nvolved n dstrbuton so we use ths converson factor wthout any 8 The REX regressons are avalable on request

17 adjustment. We combne the emssons from natural gas and fuel ol to form an estmate of total home heatng emssons. To examne the mpact of a margnal home, we repeat ths procedure usng only homes bult between 980 and Snce older homes are less fuel effcent, the average home wll overstate true energy use, especally n older areas of the country. We use only homes bult wthn the last 20 years to mnmze ths effect. In prncple, we could have used only homes bult n the last fve or ten years, but our sample szes become too small f we lmt our samples n ths way. We wll refer to these estmates as our estmates of margnal heatng emssons. Household Electrcty In the case of electrcty consumpton, we begn wth the same IPUMS-based procedure used for fuel ol and natural gas. We use state-wde prce data to convert electrcty expendture nto consumpton n megawatt hours 20. We then regress estmated electrcty consumpton on household characterstcs by metropoltan area, just as we dd for home heatng. We also follow the same mputaton procedure for owners of mult-famly unts and all renters. Followng ths strategy, we predct household annual electrcty consumpton for each metropoltan area for a standardzed household wth 2.62 people earnng an annual ncome of $62,500. In the case of electrcty, consumpton rses most sharply wth July temperatures, as shown n Fgure 3. The correlaton s relatvely strong (.6) [[[but there are some sgnfcant outlers n the Pacfc Northwest. These places have partcularly nexpensve electrcty usage, whch reflects, n part, the low costs of electrcty n that regon.]]] Ctes n the PA NW look lke they ft rght on the lne (e.g. Seattle, Portland), Tacoma uses a lttle much, but t doesn t seem outrageous. Maybe ths secton was based on a prevous graph? The converson between electrcty usage and carbon doxde emssons s consderably more complcated than the converson between natural gas or petroleum usage and emssons. If we had a natonal market for electrcty, then t would be approprate to use a unform converson factor, but snce electrcty markets are regonal, we must allow for dfferent converson factors

18 n dfferent areas of the country. There s consderable heterogenety n the emssons for megawatt hour of electrcty between areas that rely on coal, lke the Northeast, and areas that use more hydroelectrc energy, lke the West. What geographc area should we use to calculate the emssons related to electrcty usage? In prncple, one could calculate anythng from a natonal average of emssons per megawatt hour to a block specfc fgure. Usng smaller levels of geography certanly ncreases the accuracy wth whch emssons are allocated to electrcty usage. However, f electrcty s perfectly substtutable between two places, then ths precson s somewhat msleadng, and rrelevant for estmatng the margnal emssons assocated wth new constructon. The relevant consderaton s not the actual greenness of the partcular area s suppler, but rather the average emssons of the entre area. For example, consder a settng where there s a clean and a drty electrcty producer n a regon, wth dentcal costs of producton and plenty of consumers who don t care about the source of ther electrcty. In equlbrum, both producers wll generate the same amount of electrcty. A new consumer who buys only from the clean producer wll stll be assocated wth the average level of emssons. Snce these two provders are perfect substtutes, f a new resdent buys only from the clean provder, then someone else wll be buyng from the drty provder. For ths reason, t makes sense to consder the average emssons wthn the market not the ndvdual emssons of one partcular place. The North Amercan Electrc Relablty Corporaton (NERC) has dvded the U.S. nto eght electrcty markets. Whle electrcty wthn these regons s not perfectly fungble and there s some leakage across NERC regons, there s much more substtutablty of electrcty wthn NERC regons than across regons. The dffcultes nvolved n transmttng electrcty over long dstances mean that electrcty n one regon cannot readly substtute for electrcty n another regon. We therefore feel comfortable treatng these markets as more or less closed systems (Holland and Mansur 2008). We calculate NERC regon average emssons data usng power plant level data from the Envronmental Protecton Agency s egrid, or Emssons & Generaton Resource Integrated 8

19 Database data base 2. The egrid data base contans the emssons characterstcs of vrtually all electrc power n the Unted States and ncludes emssons and resource mx data for vrtually every electrcty-generatng power plant n the U.S. egrid uses data from 24 dfferent federal data sources from three dfferent federal agences: EPA, the Energy Informaton Admnstraton (EIA), and the Federal Energy Regulatory Commsson (FERC). Emssons data from EPA are ntegrated wth generaton data from EIA to create the key converson factor of pounds of carbon doxde emtted per megawatt hour of electrcty produced (lbs/mwh). Usng egrid, we calculate the emssons for megawatt hour for each of the NERC regons. There s remarkable heterogenety across these regons (Holland and Mansur, 2008). For example, San Francsco s located n a NERC regon that generates 000 pounds of carbon doxde for each megawatt hour of electrcty. In contrast, Phladelpha s located n a NERC regon where the average power plant n the regon generates 600 pounds of carbon doxde for each megawatt hour. We then use these converson factors to turn household electrcty usage nto carbon doxde emssons for each metropoltan area. We use the same converson factor to handle the electrcty consumpton of commuter rals. To consder the mpact of the margnal home, as above, we restrct our IPUMS estmates to homes bult only between 980 and Overall Household Rankngs We fnally turn to an overall rankng of metropoltan areas based on carbon doxde emssons. Table 2 lsts the 66 largest metropoltan areas for whch we have data. The frst column shows carbon doxde emssons from predcted gasolne consumpton wthn each metropoltan area. 22 There s consderable range n the consumpton of gasolne at the metropoltan area level. The New York metropoltan area s estmated to use the least gasolne, 2 see 22 These predctons are based on predctng gasolne consumpton n each census tract for a standardzed household. Wthn a metropoltan area, census tracts dffer wth respect to ther populaton densty and ther dstance to the Cty Center. Across metropoltan areas, census tracts dffer wth respect to ther MSA s regon and overall densty. We explot ths varaton as well to predct each tract s annual gasolne consumpton per household. We then use census data on household counts to weght ths tract level data nto a metropoltan area level average predcton. 9

20 whch reflects ts hgh degree of employment and populaton concentraton and ts relatvely heavy use of publc transportaton. Greenvlle, South Carolna, s estmated to have the most gasolne consumpton. The gasolne-related emssons n Greenvlle are almost twce as hgh as the gasolne-related emssons n the New York area. The second column reports our results on per household energy emssons due to publc transportaton. Ths column adds together ral and bus emssons and converts both by approprate factors to arrve at carbon doxde emssons. There s, of course, consderable heterogenety. Emssons from publc transportaton n New York Cty are more than three tons of carbon doxde from publc transt per capta. 23 However, even n New York, these emssons are relatvely modest relatve to the contrbutons of cars, snce publc transportaton shares nfrastructure, lke buses, and uses electrcty. The thrd column gves our results on fuel ol and natural gas. Agan, the results show a far amount of regonal dsparty. Detrot leads the country n home heatng emssons and Boston s a close second. Much of the West has almost no emssons from home heatng. In general, places that use fuel ol have much hgher emssons than places that use only natural gas, whch explans why emssons from ths source are much lower n Chcago than n Detrot. The fourth column shows electrcty consumpton and the ffth column shows the NERCbased converson factor for convertng electrcty nto emssons. To calculate electrcty related emssons n each area, the fourth and ffth columns need to be multpled together. 24 We show these columns separately to llustrate the role of electrcty usage versus the role of clean electrcty producton. New Orleans s the leader n electrcty usage, whle resdents of Buffalo consume the least electrcty. San Francsco has the second lowest electrcty usage n our data. 23 We do not have data on energy consumpton from publc transt n Las Vegas. 24 Households use electrcty not only at home but also where they shop and work. In results that are avalable on request, we have used the 2003 Commercal Buldng Energy Survey. Ths buldng level data set collects nformaton on roughly 5000 buldngs across the Unted States. Whle ths data set does not have metropoltan area dentfers, t does provde nformaton on the heatng degree days and coolng degree days at the locaton of each of the buldngs. We regress buldng energy consumpton per worker on buldng type dummes and these clmate measures. Usng cty level data from Burchfeld et. al. (2005), we predct commercal buldng energy consumpton per worker for each metropoltan area. The cross-metropoltan area correlaton between commercal energy consumpton predcton and our resdental energy consumpton measure s.65. On average across the metropoltan areas, commercal energy consumpton per worker s 30% hgher than resdental consumpton per household. 20

21 The sxth column sums together all of the dfferent sources of carbon doxde emssons. The table s ordered by the amount of these emssons. Calforna s ctes are blessed wth a temperate clmate and they use partcularly effcent applances and produce electrcty n partcularly clean ways. Four of the fve ctes wth the lowest emssons levels are all n Calforna. Provdence, Rhode Island ranks n the top fve due to ts low electrcty use. The hgh emssons ctes are almost all n the South. These places have large amounts of drvng and very hgh electrcty usage. Ther electrcty usage s also not partcularly clean. Texas s partcularly well represented among the places wth the hghest levels of emssons. Memphs has the absolute hghest level among our 66 metropoltan areas. Indanapols and Mnneapols are the northernmost places among our ten hghest emsson metropoltan areas. New constructon n the Northeast s generally between those extremes. These places use moderate amounts of electrcty. They drve less than Calfornans, but use large amounts of fuel ol. The Mdwest looks generally smlar to the Northeast, but larger amounts of drvng push gasolne emssons up. In column seven, we multply total emssons by 43 dollars per ton to fnd the total emssons-related externalty assocated wth an average home n each locaton. The 43 dollar number s somewhat arbtrary, and we are usng t purely for llustratve purposes. It s conservatve relatve to the Stern report (2008), whch suggests a cost of carbon doxde that s twce ths amount, but t s consderably more aggressve than the numbers used by Nordhaus (2007). Tol (2005) s one meta-study that also suggests that ths number may be somewhat too hgh whle our number s n the mddle of the range n Metcalf (2007). 26 Usng ths fgure, the range of costs assocated wth each home goes from $,48 dollars n San Dego to more than $2,05 dollars n Memphs. Ths $867 dollar gap s an annual flow, and at a dscount rate of 5 percent, ths would suggest a tax of 7,340 dollars on every new home n Memphs relatve to San Dego. The last column gves standard errors for these cost estmates. The procedure for 26 It s relevant to note that carbon tax polcy proposals have suggested taxes per ton of carbon doxde roughly n ths range. Metcalf (2007) proposes a bundled carbon tax and a labor tax decrease. As shown n hs Fgure Sx, he proposes that the carbon tax start at $5 per ton (n year 2005 dollars) now and rse by 4% a year. Under ths proposal, the carbon tax per ton of carbon doxde would equal $60 per ton (n year 2005 dollars) by

22 estmatng these standard errors s detaled n the statstcal appendx. 27 The standard errors of the carbon emssons (measured n tons) equal the standard errors of the emssons costs dvded by 43. Table 3 shows the 66 Metropoltan Area rankng based on the subset of households who lve n homes bult between 980 and Table 3 s structure s dentcal to Table 2 but Table 3 provdes an estmate of how average emssons vary across metropoltan areas for a standardzed household who lves n housng bult between 980 and Ths s useful nformaton for determnng whether wthn MSA growth patterns are shrnkng the cty s average footprnt. The dfferences between the two tables tend to offset each other. People who lve n new homes consume more gasolne, whch reflects the tendency of new growth to be n the suburbs. However, new homes are more energy effcent and therefore have lower emssons from home heatng. In general, we fnd that ths rankng based on recent growth s hghly postvely correlated wth the average rankngs reported n Table 2. The energy use dfferences between metropoltan areas are qute large. Our estmate s that a new house n coastal Calforna s assocated wth two-thrds or less of the emssons assocated wth a new house n Houston or Oklahoma Cty. These dfferences suggest that changng urban development patterns can have potentally large mpacts on total carbon emssons. Snce resdental and personal transportaton are assocated wth about 40 percent of total emssons, a 33 percent reducton n these sources would reduce total U.S. emssons by 3 percent. Of course, any polcy nterventons would mpact the flow of new housng, rather than the stock, so changes n urban development patterns would only reduce emssons gradually. Our cost estmates suggest optmal locaton-specfc taxes on development, n the absence of other carbon emsson taxes. The sx hundred dollar dfference n emssons costs between the coastal Calforna areas and Memphs suggests a flow tax of sx hundred dollars per year for each household n Memphs. Ths s not a small number. If the tax were pad n a sngle lump sum payment, of perhaps $2,000, then ths would represent a szable ncrease n the cost of lvng n 27 The standard errors for the predctons are based on the samplng varaton n the 200 NHTS data set as reported n Table. We are assumng that the large sample szes n the IPUMS data set mnmze the samplng error n our predctons of the other entres n Table 2. 22

23 Memphs. The U.S. Census tells us that the medan value of a home n Memphs n 2006 was 9,000 dollars. Of course, the model suggests that a drect carbon tax would mprove socal welfare more than any locaton tax, so we beleve that the man value of our results s only to suggest the external costs assocated wth movng to places lke Memphs. To study the cross-msa correlates of greenhouse gas producton, we present fve separate OLS regressons n Table 4. In each of these regressons, the explanatory varables nclude the logarthm of average cty ncome, the logarthm of cty populaton, average January temperature and average July temperature. We also nclude a measure of the share of cty centralzaton: the share of the populaton wthn fve mles of the cty center. The frst column shows the correlates of prvate transportaton related emssons. Income s uncorrelated wth gasolne usage at the metropoltan level. At the ndvdual level, there s a strong connecton between gasolne consumpton and ncome, but these estmates are supposed to correct for that relatonshp and they seem to do that. Larger metropoltan areas have somewhat less drvng, whch reflects the fact that these ctes are somewhat denser. As the share of populaton wthn fve mles of the cty center ncreases by 0 percent, carbon doxde emssons from drvng decreases by 300 pounds. Fnally, places wth warm Januarys have less drvng, but places wth hot Julys have more drvng. These correlatons are presumably spurous, and reflect other varables, lke the degree of sprawl, assocated wth these weather varables. The next regresson shows the correlates of publc transt emssons. In ths case, cty populaton s the only varable that s strongly correlated wth emssons. Bgger ctes are more lkely to have extensve publc transt systems. There s also a weak correlaton between ths outcome and the concentraton of populaton wthn fve mles of the cty center. The thrd regresson looks at the relatonshp between home heatng related emssons and the area-level varables. There s an extraordnarly strong negatve correlaton between ths varable and January temperature, whch was dscussed above (also see Ewng and Rong 2008). Lower July temperatures also weakly ncrease home heatng emssons. None of the other varables are strongly correlated wth ths outcome varable. The power of temperature to predct home heatng emssons explans why the r-squared for ths regresson s hgher than for any of the other regressons n ths table. 23