Local climate zones (LCZs) divide the urban landscape into homogeneous types based on urban structure (i.e., morphology of streets and buildings), urban cover (i.e., permeability of surfaces), ...construction materials, and human activities (i.e., anthropogenic heat). This classification scheme represents a standardized way of capturing the basic urban form of cities and is currently being applied globally as part of the world urban database and portal tools (WUDAPT) initiative. This paper assesses the transferability of the LCZ concept to two Ukrainian cities, i.e., Kyiv and Lviv, which differ in urban form and topography, and considers three ways to validate and verify this classification scheme. An accuracy of 64% was achieved for Kyiv using an independent validation dataset while a comparison of the LCZ maps with the GlobeLand30 land cover map resulted in a match that was greater than 75% for both cities. There was also good correspondence between the urban classes in the LCZ maps and the urban points of interest in OpenStreetMap (OSM). However, further research is still required to produce a standardized validation protocol that could be used on a regular basis by contributors to WUDAPT to help produce more accurate LCZ maps in the future.
There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated ...field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture.
This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo‐Wiki application. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available.
Ongoing deforestation is a pressing, global environmental issue with direct impacts on climate change, carbon emissions, and biodiversity. There is an intuitive link between economic development and ...overexploitation of natural resources including forests, but this relationship has proven difficult to establish empirically due to both inadequate data and convoluting geo-climactic factors. In this analysis, we use satellite data on forest cover along national borders in order to study the determinants of deforestation differences across countries. Controlling for trans-border geo-climactic differences, we find that income per capita is the most robust determinant of differences in cross-border forest cover. We show that the marginal effect of per capita income growth on forest cover is strongest at the earliest stages of economic development, and weakens in more advanced economies, presenting some of the strongest evidence to date for the existence of at least half of an environmental Kuznets curve for deforestation.
Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best ...practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO
2
), gridded EI Open-source Data Inventory for Anthropogenic CO
2
(ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges.
The development of high-resolution greenhouse gas (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 residential sector by settlements at a fine spatial resolution. 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.
Ending energy poverty is a necessary condition for achieving the Sustainable Development Goals (SDGs). Boosting electricity access levels is, however, insufficient if consumption and reliability ...indicators stagnate. Previous research has shown that satellite-derived data can complement field surveys in tracking energy poverty but with little consideration for the multi-dimensionality of energy access and the role of demographic dynamics. Here, we process 6 years of high-resolution population, nighttime light, and settlement data for sub-Saharan Africa to derive multi-dimensional estimates of electricity access. Our results, validated against a range of sources, confirm a recent surge in electrification such that >115 million people gained access over the 2014–2019 period. Yet, they reveal wide inequalities in the quality of electrification, which cannot be observed in the existing statistics. The pace of electrification must more than triple to fulfill SDG 7.1.1 by 2030. Efforts could fall short if aimed solely at boosting numbers of national electricity connections.
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•115+ million in sub-Saharan Africa gained access to electricity in 2014–2019•Most electrified households benefit from low-tier electricity access•The pace of electrification must more than triple to fulfill SDG 7.1.1 by 2030•Inequalities may persist if efforts solely aim at increasing new connections
Energy poverty is widely diffused and persistent in sub-Saharan Africa. Even in areas that formally have electricity access, power consumption and supply reliability are largely inadequate. Yet, most institutional statistics fail to capture these different dimensions and rely on rapidly outdated and unwieldy household surveys. In this study, we process high-resolution population distribution maps (including demographic and migration trends), satellite-measured nighttime light, and settlement information for sub-Saharan Africa. This allows us to derive multi-dimensional estimates of electricity access over space and time and compare them with a set of published records. Our results reveal wide inequalities in the pace and quality of electrification, which cannot be observed in existing statistics. We show that the pace of electrification must more than triple to fulfill SDG 7.1.1 and discuss why electrification policy could fall short if aimed solely at boosting electricity connections.
Energy poverty is widely diffused and persistent in sub-Saharan Africa. Even in areas that formally have access to electricity, power consumption and supply reliability are largely inadequate. We derive multi-dimensional estimates of electricity access over space and time by processing high-resolution population distribution maps, satellite-measured nighttime light, and settlement locations for sub-Saharan Africa. Our results reveal wide inequalities in the pace and quality of electrification, which cannot be observed in existing statistics.
Quantifying greenhouse gas emissions Jonas, Matthias; Bun, Rostyslav; Nahorski, Zbigniew ...
Mitigation and adaptation strategies for global change,
08/2019, Letnik:
24, Številka:
6
Journal Article
Recenzirano
Odprti dostop
The assessment of greenhouse gases (GHGs) and air pollutants emitted to and removed from the atmosphere ranks high on international political and scientific agendas. Growing international concern and ...cooperation regarding the climate change problem have increased the need to consider the uncertainty in inventories of GHG emissions. The approaches to address uncertainty discussed in this special issue reflect attempts to improve national inventories, not only for their own sake but also from a wider, system analytic perspective. They seek to strengthen the usefulness of national emission inventories under a compliance and/or global monitoring and reporting framework. The papers in this special issue demonstrate the benefits of including inventory uncertainty in policy analyses. The issues raised by the authors and featured in their papers, along with the role that uncertainty analysis plays in many of their arguments, highlight the challenges and the importance of dealing with uncertainty. While the Intergovernmental Panel on Climate Change (IPCC) clearly stresses the value of conducting uncertainty analyses and offers guidance on executing them, the arguments made here in favor of performing these studies go well beyond any suggestions made by the IPCC to date. Improving and conducting uncertainty analyses are needed to develop a clear understanding and informed policy. Uncertainty matters and is key to many issues related to inventorying and reducing emissions. Considering uncertainty helps to avoid situations that can create a false sense of certainty or lead to invalid views of subsystems. Dealing proactively with uncertainty allows for the generation of useful knowledge that the international community should have to hand while strengthening the 2015 Paris Agreement, which had been agreed at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). However, considering uncertainty does not come free. Proper treatment of uncertainty is demanding because it forces us to take the step from “simple to complex” and to grasp a holistic system view. Only, thereafter, can we consider potential simplifications. That is, comprehensive treatment of uncertainty does not necessarily offer quick or easy solutions for policymakers. This special issue brings together 13 papers that resulted from the 2015 (4th) International Workshop on Uncertainty in Atmospheric Emissions, in Cracow, Poland. While they deal with many different aspects of the uncertainty in emission estimates, they are guided by the same principal question: “What GHGs shall be verified at what spatio-temporal scale to support conducive legislation at local and national scales, while ensuring effective governance at the global scale?” This question is at the heart of mitigation and adaptation. It requires an understanding of the entire system of GHG sources and sinks, their spatial characteristics and the temporal scales at which they react and interact, the uncertainty (accuracy and/or precision) with which fluxes can be measured, and last but not least, the consequences that follow from all of the aforementioned aspects, for policy actors to frame compliance and/or global monitoring and reporting agreements. This bigger system context serves as a reference for the papers in the special issue, irrespective of their spatio-temporal focus, and is used as a guide for the reader.
Agricultural activity plays a significant role in the atmospheric carbon balance as a source and sink of greenhouse gases (GHGs) and has high mitigation potential. The agricultural emissions display ...evident geographical differences in the regional, national, and even local levels, not only due to spatially differentiated activity, but also due to very geographically different emission coefficients. Thus, spatially resolved inventories are important for obtaining better estimates of emission content and design of GHG mitigation processes to adapt to global carbon rise in the atmosphere. This study develops a geoinformation approach to a high-resolution spatial inventory of GHG emissions from the agricultural sector, following the categories of the United Nations Intergovernmental Panel on Climate Change guidelines. Using the Corine Land Cover data, a digital map of emission sources is built, with elementary areal objects that are split up by administrative boundaries. Various procedures are developed for disaggregation of available emission activity data down to a level of elementary emission objects, conditional on covariate information, such as land use, observable in the elementary object scale. Among them, a statistical scaling method suitable for spatially correlated areal emission sources is applied. As an example of implementation of this approach, the spatial distribution of methane (CH
4
) and Nitrogen Oxide (N
2
O) emissions was obtained for areal emission sources in the agriculture sector in Poland with a spatial resolution of 100 m. We calculated the specific total emissions for different types of animal and manure systems as well as the total emissions in CO
2
-equivalent. We demonstrated that the emission sources are located highly nonuniformly and the emissions from them vary substantially, so that average data may provide insufficient approximation. In our case, over 11% smaller emission was estimated using spatial approach as compared with the national inventory report where average data were used. In addition, we quantified uncertainties associated with the developed spatial inventory and analysed the dominant components in total emission uncertainties in the agriculture sector. We used the activity data from the lowest possible (municipal) level. The depth of disaggregation of these data to the level of arable lands is minimal, and hence, the relative uncertainty of spatial inventory is smaller when comparing with traditional gridded emissions. The proposed technique allows us to discuss factors driving the geographical distribution of GHG emissions for different categories of the agricultural sector. This may be particularly useful in high-resolution modelling of GHG dispersion in the atmosphere.
Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially ...explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and other GHG compounds (e.g., CO
2
, CH
4
, N
2
O, SO
2
, NMVOC) as well as total emissions in the CO
2
-equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Such an approach is flexible, provided the data are available, and can be applied to other countries.
Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing ...land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.
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