Received: 13 August 2018 / Accepted: 25 January 2019/ # The Author(s) 2019
The development of high-resolutiongreenhousegas (GHG) inventories is an important step towards emission reduction in different sectors. However, most of the spatially explicit approaches that have been developed to date produce outputs at a coarse resolution or do not disaggregate the data by sector. In this study, we present a methodology for assessing GHG emissions from the residentialsector by settlements at a fine spatialresolution. In many countries, statistical data about fossil fuel consumption is only available at the regional or country levels. For this reason, we assess energy demand for cooking and water and space heating for each settlement, which we use as a proxy to disaggregate regional fossil fuel consumption data. As energy demand for space heating depends heavily on climatic conditions, we use the heating degree day method to account for this phenomenon. We also take the availability of energy sources and differences in consumption patterns between urban and rural areas into account. Based on the disaggregated data, we assess GHG emissions at the settlement level using country and regional specific coefficients for Poland and Ukraine, two neighboring countries with different energy usage patterns. In addition, we estimate uncertainties in the results using a Monte Carlo method, which takes uncertainties in the statistical data, calorific values, and emission factors into account. We use detailed data on natural gas consumption in Poland and biomass consumption for several regions in Ukraine to validate our approach. We also compare our results to data from the EDGAR (Emissions Database for Global Atmospheric Research), which shows high agreement in places but also demonstrates the advantage of a higher resolution GHG inventory. Overall, the results show that the approach developed here is universal and can be applied to other countries using their statistical information.
technologies will be necessary. Low-carbon energy sources, high-efficiency systems (e.g. district energy) and the elimination of fossil fuel-based heating are needed in each jurisdiction if Canadian cities are to achieve carbon neutrality. Differing scales of investment will be required in each region to achieve these goals. While short-term policy to promote the switching of heating to electric sources (geoexchange or air-source heat pumps) may result in higher emissions in jurisdictions with carbon-intensive electricity grids, there may be a short-term benefit to their adoption due to the growth of technical expertise, as well as greater public awareness and acceptance. Though the elimination of fossil fuel-based heating energy services within the residentialsector will likely require increased electrical grid capacity and overall electricity demand, there is significant potential to meet this increase with low-carbon alternatives in the long-term (i.e. small-scale hydro, wind, solar, geothermal). Leadership for low-carbon electricity strategies must be taken with the ultimate goal of providing a clean grid (which is generally becoming more economically attractive as solar PV and wind prices continue to decline; Hurlbut et al 2013), given the need to drastically reduce GHG emissions in developed nations, and the potential for increases in total residential electricity demand as population and adoption of consumer electronics increase. The importance of behavioural change cannot be overlooked, since even aggressive technology adoption may not produce emission reductions on the scale desired (Mohareb and Kennedy 2013). Reducing space conditioning demand through more sophisticated thermostats and improved energy use feedback technologies represent two ways that GHG reduction can be achieved through behaviour changes (Moon and Han 2011; Granderson et al 2011).
Table 13 shows us that geoexchange systems and natural gas present similar situations with regard to GHG emissions for the province’s residentialsector. According to the hypotheses used in this study, which are aimed at establishing both interprovincial and intraprovincial comparisons, we note a marginal minimal advantage for natural gas, since the electricity used by the heat pump of a geoexchange system comes essentially from coal sources and thus emits a large amount of GHGs. 4 It should be noted that a geoexchange system with a high COP compared to a high efficiency gas furnace whose theoretical performance is reduced by a lack of maintenance will make geoexchange more attractive from a GHG emissions perspective.
We used the final output (available at: http://avaa.tdata.fi/ web/cbig/gpan) of a comprehensive global analysis that ranked the world’s currently unprotected land according to its potential for expanding and filling gaps in the current PA net- work as stated by the Aichi Target 11 of the CBD (Pouzols et al., 2014). The underlying original data used in this study to derive the PA expansion map included range maps of all red- listed terrestrial vertebrates (24 757 species assessed under the IUCN red list) and the areas covered by each of the world’s 827 ecoregions as defined by WWF (World Wide Fund for Nat- ure). In the analysis, species were weighted based on their threat status, and species ranges were filtered by present and predicted land-use intensity (Van Asselen & Verburg, 2013). The analysis took as a starting point the current PA network (the World Database on Protected Areas) and used the spatial conservation prioritization tool Zonation v.4 to identify the pri- ority areas for PA network expansion to 17% of the global land area (Moilanen et al., 2005, 2014). The process iteratively ranks all areas from lowest to highest priority for conservation, guided by principles such as balance between representation of all input features, minimization of aggregate extinction rates and preference for spatial aggregation (Pouzols et al., 2014).
Oda et al. ( 2019 ) compare ODIAC (Open-source Data Inventory for Anthropogenic CO 2 ) with GESAPU, a high-resolution, spatially explicit emission inventory —here, the one provid- ed by Bun et al. ( 2018 ) for Poland. ODIAC is itself a global inventory with a spatialresolution of 1 km × 1 km, based on the disaggregation of the national annual fossil-fuel CO 2 emission estimates provided by the Carbon Dioxide Information Analysis Center. To achieve that highspatialresolution, ODIAC uses point source information (source points’ geographical location and CO 2 emissions) and satellite nightlight (radiance) data. Because of its greater local “realism”, GESAPU is used as a reference in this comparison. The difference between the two inventories is understood to serve as a proxy for errors and uncertainties associated with ODIAC. This difference is small for total emission estimates of countries (2.2%), point sources (0.1%), and non-point sources (4.5%). However, it increases toward smaller spatial scales, indicating that disaggregation error and uncertainty increase. Oda et al. find a difference (relative at the pixel level) of typically about 30% for urban areas, up to 90 –100% for urban-rural transition areas, and 10% for remote areas. The difference decreases with increas- ing spatial aggregation by approximately 70% for spatial scales, which are typical for global and regional transport models (50 km and greater). Based on their findings for Poland, the authors envisage using ODIAC globally to support monitoring verification and even at subnational levels —it is not unusual for countries to run emission inventories at the state or provincial levels while reporting only national emissions to the UNFCCC. However, as noted by the authors, such a request would need to accompany concerted global actions, ranging from the collection and reporting of data, through monitoring, to international governance.
According to the table 3 and figure 4, it can be concluded that the highest energy consumption for all types of energy existed in highresidential density, whereas the lowest energy consumption existed in low residential density. Although the lowest average household energy consumption existed in medium residential density, the total energy consumption in medium density is higher than that in low residential density due to the number of households. Figure 4 also shows that there is significance difference of the total energy consumption in each residential density. As a result, the level of greenhousegasemissions (CO2) in each residential density is also different, as shown in the table 4. It also shows that the highest total production of GHG emissions existed in highresidential density due to the total of energy consumption. In reverse, the lowest total production of GHG emissions existed in low residential density Table 4: Total GHG Emissions based on Residential Density in Household Sector
The use of GIS to store data and compute emissions has allowed this assessment to maintain the original spatialresolution of data sources, and to thus avoid generalising and averaging input data where spatially explicit sources were available. Two main methodological innovations have also been made compared to previous analyses. One is the development of a herd model that computes the “dairy related stock”, consisting of the cattle required to maintain a population of milked cows and the “surplus” calves that are fattened for meat production. The second is a feed basket computation module that links locally available feed resources with animal numbers and productivity. These modules allow estimation of information which is required for the assessment, but is not available in statistical databases, and they also ensure coherence between the production parameters (e.g. reproduction and herd size, or feed intake and milk yields). Despite these methodological advances, the assessment relies on numerous assumptions and simplifications, as well as methodological choices that influence the results. The sensitivity analysis has shown that the emissions, per kg of milk and meat, are mostly affected by digestibility, milk yield per cow and manure management. The supporting uncertainty analysis, which assessed random variations in input parameters and emissions factors, showed that emissions can range to plus and minus 26 % of the average emissions per unit of milk.
the root of the sum of squares of the error in the underlying sources. Strictly speaking, this is only valid if the uncertainties meet the following conditions: a) standard-normal division (‘Gaussian’), b) 2σ smaller than 60%, c) sector to sector, sub- stance to substance are independent. Indeed for a number of sources it is clear that activity data or emission factors are correlated, which increases the overall uncertainty of the sum to an unknown extent. Also, for some sources it is already known that the probability distribution is not normal; in par- ticular when uncertainties are very high (order of 100%) it is clear that the distribution will be skewed towards zero. Even more important is that, although the uncertainty estimates have been based on the documented uncertainties menti- oned above, unavoidably uncertainty estimates are in the end based on expert judgement of representativity for the Netherlands’ circumstances of the particular source category. Sometimes, however, only limited reference to actual Nether- lands data was possible to support these estimates. Focus- sing on the order of magnitude of the individual uncertainty estimates we believe that this dataset provides a reasonable first assessment of the uncertainty of key source categories in the Netherlands. Furthermore, (..) we have neglected the uncertainty introduced by the emissions from the sources of the ER-I (Individually reporting firms), of which the uncer- tainty is actually unknown. These sources in the Emission Registration account for about half of the total CO2 emissions in the Netherlands (..). However, as described in Chapter 4, total CO2 emissions per industrial sub-sector cannot be off from the reference calculation by more than 5% (in practice, the group total may show much less deviation).”
Moreover, even if significant areas of poorly drained land with high CH 4 emissions are present within the
simulated area, large changes in those CH 4 emission
rates resulting from conversion to bioenergy crops are only likely to occur if the land is drained for bioenergy crops. We are not aware of any planned or actual drai- nage of extensive areas of land for bioenergy crops. Drainage is unlikely to take place on soils currently under rotational crops because the land will already have been drained (if it was necessary). Also, SRC wil- low and poplar are suitable for planting on soils with a shallow water table (1–2 m deep), with willow able to cope with water-logging, making it suitable for planting in areas with a high water table or areas prone to flood- ing (Hall, 2003). SRC therefore provides a bioenergy option that is unlikely to require the drainage of water- logged land.
Table 4 shows detailed county-level results re- lated to Washington, Poinsett, and White coun- ties. These counties were chosen to show the im- pact of limited land use substitution possibilities as well as historical harvested acre limitations. Washington County, while profitable with hay and pasture production, has no history of row crop production, and hence the economic choice to re- duce state-level emission reductions begins to im- pact that county at the 10 percent emissions re- duction level when hay production declines. At the 20 percent emissions reduction target, pasture acres (not counted in historical harvested acres but tracked separately) go to their county mini- mum to enable a 20 percent state reduction in emissions even though these acres are still profit- able and pasture is the third lowest emitting crop (Figure 1 and Table 2). Hence, the response of Washington County with limited land use substi- tution possibilities is to curtail production only to allow more carbon-efficient counties to maintain their output level or marginally decrease their out- put to a lesser extent than Washington County. The second county analyzed in Table 4 is Poin- sett County, with the largest historical harvested acres and with the greatest number of possible choices for land use substitution. Note that in this county, production of non-irrigated soybeans—the lowest emitting crop (Figure 1 and Table 2)—and hay increases at the cost of wheat acreage to cur- tail emissions by 5 percent. Analyzing the 10 per- cent and 20 percent emissions scenario suggests a reduction of rice acreage to its historical mini- mum while adding additional non-irrigated soy- bean acres and dropping the initially added hay acres for non-irrigated soybeans with lower emis- sions. Notable, in this county is the relatively high level of profitability per acre across all enter- prises. The most carbon efficient (highest NR/lb of carbon emitted) crops stay unchanged by emis- sion restrictions, as other counties offer emission reductions at a lower overall cost to state returns and hence overall harvested acreage in the total acres column remains at historical maximum acres. White County, which has an intermediate level of land use substitution possibilities, provides
In 2012, approximately 251 million tons of municipal solid waste (MSW) were generated in the U.S.; of which about 135 million tons (53.8% of total generation) were discarded in landfills (U.S. EPA, 2014a). Landfilling is the leading waste management practice in the U.S., followed by recycling and recovering (34.5 %), and combustion with energy recovery (11.7 %) (U.S. EPA, 2014a). The effectiveness of MSW management practices has been evaluated in the published literature and assessment models based on their greenhousegas (GHG) emissions; economic costs; and airborne, soil, and waterborne emissions (Weitz et al., 1999; Weitz et al., 2002; Consonni et al., 2005; Winkler and Bilitewski, 2007; Buttol et al., 2007; Cherubini et al., 2009). However, the direct and indirect impacts of MSW management practices on water resources have been neglected or not fully considered. Today, the world is challenged by a water crisis threatening global peace, health, and economic development (Bigas, 2012). Many parts of the world struggle with limited water resource availability to sustain growing populations, higher consumption rates, pollutant loadings, and demands of industries, energy sectors, and businesses. The recent U.S. droughts, which affected more than 50% of the U.S. (Fuchs, 2012), have drawn attention to the increasing scarcity of water and the need for sustainable water management strategies.
The present paper departs from the previous literature mainly because of the focus on the hetero- geneity of abatement costs within the EU and on the implications of this heterogeneity for the design of a mitigation policy. Abatement cost heterogeneity is indeed crucial for both economic and policy purposes. The heterogeneity of abatement costs is a fundamental determinant in the optimal choice of a mitigation policy instrument. Acknowledgedly, incentive-based instruments are generally viewed –at least under perfect information– as more efficient than command-and-control regulations and uniform standards. Incentive-based instruments tend to equalize marginal abatement costs across polluting agents and consequently minimize the total abatement cost. In contrast, uniform standards generally result in distorted allocations of the total abatement. Nevertheless, information and control costs can jeopardize the implementation of optimal instruments in practice, more particularly if spatial hetero- geneity is large. There is thus a trade-off between control costs of implementing optimal instruments on the one hand, and the efficiency loss due to distorted abatement allocation on the other hand (see for instance Antle et al. (2003) for an application to the design of carbon sequestration contracts). Newell and Stavins (2003) analytically investigate the savings of incentive-based instruments relative to uniform standards. As expected, these savings are shown to increase with respect to the variance of marginal abatement costs 3 . Furthermore, in practice policymakers attach at least as much impor- tance to the spatialdistribution of economic and environmental impacts of a mitigation policy as to the magnitude of these impacts. Spatial analyzes that go beyond EU- or country-wide estimates of abatement costs curves are hence needed. The interest of such a spatial approach is strengthened
Podaci o emisijama stakleničkih plinova antropogenog podrijetla razlikuju se prema pojedi-nim izvorima. Tako su, primjerice, ukupne emisije ugljičnog dioksida prema “CDIAC” (United States Department of Energy Carbon Dioxide Information Analysis Center) za 2010. g. u svijetu iznosile 33.509 milijuna tona (List of countries by Carbon dioxide emissions – Wikipedia, od 05. 11. 2013.), a prema izvoru “EDGAR” (Emissions database for global atmospherich research – European Commission, od 05. 02. 2014.), ukupne su emisije stakleničkih plinova u 2010. g. iznosile 50.101 milijuna tona, te bi izračunati udio ugljičnog dioksida iznosio tek 67 % u odnosu na stvarno utvrđen udio od 76 % ili više (tablica 2). S obzirom na ocjenu da su podaci CDIAC-a nepotpuni (nisu obuhvaćene sve zemlje, odnosno područja svijeta) ubuduće ćemo se referirati na podatke EDGAR-a (obuhvaćene su 223 zemlje, odnosno područja svijeta).
118. The typical / average capacities and average payloads agreed with DfT that are used in the calculation of van emission factors per tonne km are presented in Table 32. These are based on quantitative assessment of the van database used by AEA in variety of policy assessment for DfT. For the 2011 update, a correction has been made to the dataset used to calculate van emissions in 2010, where it was discovered some van models had been included in the incorrect weight classes. The correction reallocated some vans between the different weight categories for the payload capacity calculation. In addition the assumed split of petrol van stock between size classes has been adjusted using the split of registrations from this dataset. This has resulted in some changes to emission factors, particularly since the proportion of smaller petrol vans is much higher..
Total general government expenditure is defined in ESA-95 §8.99 by reference to a list of categories: intermediate consumption, gross capital formation, compensation of employees, other taxes on production, subsidies, payable property income, current taxes on income, wealth, etc., social benefits, some social transfers, other current trans- fers, some adjustments, capital transfers and transactions on non-produced assets. Unit: millions of € per 1.000.000 € GDP. Data were obtained from Eurostat (Eurostat, 2014). We have chosen total government expenditure because according to some au- thors (e.g. Bernauer and Koubi, 2006) government size and GHG and other emissions are closely connected. An expansion in government size is unambiguously associated with welfare improving for society as a whole; namely, i.e. when this expansion is demand-driven (citizen-over-state) and when it aims at the provision of a pure pub- lic good or the correction of an externality. The study of Bernauer and Koubi (2006) examined the relationship between government spending and air emissions due to GHG and other emissions in 42 countries over the 1971-1996 period. Their key finding was that countries with a larger government spending tend to suﬀer from more emis- sions into the air. However, a large body of literature (e.g. Grossman and Krueger, 1993) demonstrates the opposite results that follow the Kuznets curve theory.
Changes in behaviour and attitudes towards eating are also essential (Garnett, 2008). Reduced con- sumption of the most harmful food products and of products with low nutritional value, avoidance of food waste and eating only as much as necessary are actions that can reduce CO2 emissions from food consumption and simultaneously combat other environmental or social problems, such as obe- sity and food provision inequity. In fact, more sustainable diets are often in line with healthy diet rec- ommendations by governmental authorities.
lArgely regionAl nAturAl gAs mArkets In contrast to oil, which is widely traded across national boundaries and over long distances, natural gas has been primarily a domestic resource. The low density of natural gas makes it difficult to store and to transport by vehicle (unless the gas is compressed or liquefied). (See chapter 8 for an extended discussion of liquefied and compressed natural gas.) Natural gas is therefore transported via pipelines that connect the natural gas wells to end consumers. Trade patterns tend to be more regional (particularly in the United States), and prices tend to be determined within regional markets. On the world stage, resources are concentrated geographically. Seventy percent of the world’s gas supply (including unconventional resources) is located in only three regions—Russia, the Middle East (primarily Qatar and Iran), and North America. Within the United States, 10 states or regions account for nearly 90 percent of produc- tion: Arkansas, Colorado, Gulf of Mexico, Louisiana, New Mexico, Oklahoma, Pennsylvania, Texas, Utah, and Wyoming. Significant barriers exist to establishing a natural gas market that is truly global. While most natural gas supplies can be developed economically with relatively low prices at the wellhead or the point of export, 21 high transportation costs—either via long-
Personal carbon allowances and trading is a fourth way forward. This tackles the distributional dilemma head-on by instituting a form of carbon rationing coupled with trading. There is a wide variety of such proposals, but all entail a cap on a country’s total GHG emissions (decreasing year by year) and a division of this amount into equal annual allowances for each adult resident (usually with a lower allowance for each child) (see for example Committee on Personal Carbon Trading 2008). In effect a dual accounting standard and currency is developed – energy has both a money price and a carbon ‘price’. Those who emit less carbon than the average could sell their surplus and gain, while higher emitters would pay a market price for their excess. Advocates claim many benefits: a PCAT scheme covering domestic energy, road fuel and air travel would be on average quite progressive; it would make real the carbon rationing required and could bring about behavioural change more directly and quickly. It could be implemented using personal carbon cards and smart metering, though the administrative difficulties should not be underestimated. In effect it would constitute a carbon form of the Basic Income idea, and could have similar benefits by redistributing income while not harming disincentives to work; indeed it would likely have more legitimacy than a basic cash income.
To remove the built-in flaws, one option is to use eligibility to trade as an enforcement mechanism. This approach reflects the view that in some cases prevention of non- compliance is more effective than ex post reward or punishment. It assumes that trading is a privilege, not a right. Initially, trading is only allowed to those “eligible” parties whose domestic monitoring, tracking and enforcement systems have met certain “minimum quality” criteria. The criteria include, but are not limited to, compliance with inventory and reporting obligations of Articles 5 and 7 of the Kyoto Protocol, and establishment and maintenance of a satisfactory national registry that accurately records all holdings, transfers and acquisitions of AAUs by the Party and all the legal entities that it authorises to trade. The eligibility requirements would be particularly important if ex post penalties for non- compliance were weak or unavailable in practice. By precluding those Annex B countries that do not meet the criteria from engaging in emissions trading until such time as they bring their domestic monitoring and enforcement systems up to the threshold eligible for trading, the eligibility criteria would ensure that there is no significant risk to buyers, thus giving the credibility of the emissions trading system. The more stringent are the criteria, the greater is the assurance that traded tons of emissions represent real reductions, the less risk there is to buyers, and hence the more likely buyers become active on the market. From the environmental perspective, the more stringent criteria are preferred. On the other hand, less developed Annex B countries or new entrants to Annex B are less likely to have well- developed monitoring and enforcement systems in place, but are most likely to have surplus emissions permits to sell. The more stringent eligibility requirements would preclude these potential sellers of emissions permits and increase buyers’ costs of compliance, thus undermining the effectiveness of emissions trading in lowering the cost of abating GHG emissions. Clearly, there is a trade-off between the desirability of assigning the seller responsibility for the validity of acquired permits and the “appropriate” eligibility threshold. In addition to using eligibility to determine which Parties could be eligible for trading, the eligibility requirements could demand the suspension of valuable trading rights of those Parties that are not in compliance with their targets in the previous commitment period once subsequent commitment periods begin to take effect. If adopted, this would promote continuing compliance.
* This article is based on a project that was commissioned by the National Emissions Trad- ing Taskforce (NETT) in 2007. The NETT was institutionalised by the Australian State and Territory Governments in 2004 in order to develop a multi-jurisdictional emissions trading scheme. The authors of the article were part of a consortium that was engaged to provide the NETT with detailed qualitative input on the auction design. In its 2008 White Paper, the new Federal Government adopted most of our recommendations for the auctions (Commonwealth of Australia 2008). The authors thank Evans & Peck for managing the project, Karl-Martin Ehrhart for fruitful discussions, as well as Ritwik Bose and Oli Sartor for research assistance. They also thank the participants of the 2007 workshops on permit auctions organised by the Centre for Energy and Environmental Markets at the UNSW. Support from the Environmen- tal Economics Research Hub ﬁnanced by the Commonwealth Environmental Research Facili- ties (CERF) and the Economics Design Network is gratefully acknowledged. The authors would also like to thank three anonymous referees as well as the editor for valuable suggestions that helped to improve the paper.