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We present regionally aggregated emissions of greenhouse gases (GHG) from five land cover categories in Finland: artificial surfaces, arable land, forest, waterbodies, and wetlands. Carbon (C) sequestration to managed forests and unmanaged wetlands was also assessed. Models FRES and ALas were applied for emissions (CO2, CH4, N2O) from artificial surfaces and agriculture, and PREBAS for forest growth and C balance. Empirical emission coefficients were used to estimate emissions from drained forested peatland (CH4, N2O), cropland (CO2), waterbodies (CH4, CO2), peat production sites and undrained mires (CH4, CO2, N2O). We calculated gross emissions of 147.2 ± 6.8 TgCO2eq yr−1 for 18 administrative units covering mainland Finland, using data representative of the period 2017–2025. Emissions from energy production, industrial processes, road traffic and other sources in artificial surfaces amounted to 45.7 ± 2.0 TgCO2eq yr−1. The loss of C in forest harvesting was the largest emission source in the LULUCF sector, in total 59.8 ± 3.3 TgCO2eq yr−1. Emissions from domestic livestock production, field cultivation and organic soils added up to 12.2 ± 3.5 TgCO2eq yr−1 from arable land. Rivers and lakes (13.4 ± 1.9 TgCO2eq yr−1) as well as undrained mires and peat production sites (14.7 ± 1.8 TgCO2eq yr−1) increased the total GHG fluxes. The C sequestration from the atmosphere was 93.2 ± 13.7 TgCO2eq yr−1. with the main sink in forest on mineral soil (79.9 ± 12.2 TgCO2eq yr−1). All sinks compensated 63% of total emissions and thus the net emissions were 53.9 ± 15.3 TgCO2eq yr−1, or a net GHG flux per capita of 9.8 MgCO2eq yr−1. [ABSTRACT FROM AUTHOR]

Air pollution is an important part of ecological environment problems. Gaseous energy combustion pollution is a key part of air pollution. Analysis of its spatial distribution and drivers is important to effectively manage air quality and promote regional green development. This paper uses panel data of 30 provinces in China from 2006 to 2019 to analyze the spatial distribution and drivers of gaseous energy combustion pollution in each province of China through a combination of quantitative and qualitative analysis based on the spatial Durbin model, and further decompose the spatial effects of each influencing factor. The empirical results show, firstly, that inter-provincial gaseous energy combustion pollution in China shows significant spatial differences and cluster characteristics. Secondly, most areas are located in high–high aggregation area and low–low aggregation area. Finally, population, energy consumption and other independent variables have different impacts on gaseous energy combustion pollution in different regions. The changes of these variables will affect the adjacent areas through spatial transmission while affecting a region. According to the empirical results, this study proposes policy recommendations to balance the pollution degree of gaseous energy combustion in various regions and effectively reduce the emissions of various pollution sources. The research results are helpful to understand the spatial distribution of gaseous energy combustion pollution in various regions of China, provide reference for the future development of various regions, and have important practical significance for the reduction and balance of pollution levels. [ABSTRACT FROM AUTHOR]

Measures of national financial inclusion mostly focus on traditional financial services and ignore the role of digital finance. We incorporate several digital elements of financial inclusion and construct a comprehensive index of digital financial inclusion for 101 countries in 2017. The spatial distribution of the index shows a strong geographical aggregation and a clustered pattern in national income groups. Regions with higher-income countries tend to have better digital financial inclusion. However, digital technology has helped several low-income countries to improve their financial inclusion. But this improvement does not spillover to other low-income countries in the region. [ABSTRACT FROM AUTHOR]

Abstract Multiregional input–output (MRIO) databases are used to analyze the impact of resource use and environmental impacts along global supply chains. To accurately account for pressures and impacts that are highly concentrated in specific sectors or regions of the world, such as agricultural and land-use-related impacts, MRIO databases are being fueled by increasingly more detailed data. To date no MRIO database exists which couples a high level of harmonized sector detail with high country resolution. Currently available databases either aggregate minor countries into rest-of-the-world (WIOD and EXIOBASE 3), or the high country resolution is achieved at the cost of non-harmonized or lower sectoral detail (Eora, OECD-ICIO or the GTAP-MRIO). This aggregation can cause potentially significant differences in environmental and socioeconomic impact calculations. In this paper, we describe the development of an EXIOBASE 3 variant that expands regional coverage from 49 regions to 214 countries, while keeping the high and harmonized sectoral detail. We show the relevance of disaggregation for land-use accounting. Previous rest-of-the-world regions supply one-third of global land, which is used to produce a large range of different products under very different levels of productivity. We find that the aggregation of regions leads to a difference in the balance of land embodied in trade of up to 6% and a difference of land embodied in imports of up to 68% for individual countries and up to 600% for land-use-relevant sectors. Whilst the database can still be considered experimental, it is expected to increase the accuracy of estimates for environmental footprint studies of the original EXIOBASE countries, and provides the first estimates for the countries in the previous rest-of-the world.

A new partition of geographic regions in the Eastern US, based on environmental variables rather than political borders, is introduced as an alternative approach to aggregation of consumer demand data. The classification exercise described in this paper led to the definition of twelve distinct geographic regions representing the same area as seventeen corresponding Western States in Holman (2020). The same exercise is repeated here to define twelve distinct regions representing the same area as thirty-one corresponding Eastern States and the District of Columbia. The purpose of this classification exercise in both cases is to create Consumer Ecoregions (CERs) that, in comparison to the partition of states, demonstrate significantly less variability in terms of population, economic output, and land area while better representing unique climates and landscapes. This serves the broader goal of developing a partition for the entire conterminous United States which can be used to analyze business and economic data through a geographic lens that provides a more concise view of distinct regions. [ABSTRACT FROM AUTHOR]

Multiregional input–output (MRIO) databases are used to analyze the impact of resource use and environmental impacts along global supply chains. To accurately account for pressures and impacts that are highly concentrated in specific sectors or regions of the world, such as agricultural and land-use-related impacts, MRIO databases are being fueled by increasingly more detailed data. To date no MRIO database exists which couples a high level of harmonized sector detail with high country resolution. Currently available databases either aggregate minor countries into rest-of-the-world (WIOD and EXIOBASE 3), or the high country resolution is achieved at the cost of non-harmonized or lower sectoral detail (Eora, OECD-ICIO or the GTAP-MRIO). This aggregation can cause potentially significant differences in environmental and socioeconomic impact calculations. In this paper, we describe the development of an EXIOBASE 3 variant that expands regional coverage from 49 regions to 214 countries, while keeping the high and harmonized sectoral detail. We show the relevance of disaggregation for land-use accounting. Previous rest-of-the-world regions supply one-third of global land, which is used to produce a large range of different products under very different levels of productivity. We find that the aggregation of regions leads to a difference in the balance of land embodied in trade of up to 6% and a difference of land embodied in imports of up to 68% for individual countries and up to 600% for land-use-relevant sectors. Whilst the database can still be considered experimental, it is expected to increase the accuracy of estimates for environmental footprint studies of the original EXIOBASE countries, and provides the first estimates for the countries in the previous rest-of-the world. [ABSTRACT FROM AUTHOR]

Wind power is one of the most established renewable power resources yet it is also one of the most volatile resources. This poses a key challenge for successfully integrating wind power at a large scale into the power grid. Here we present an analysis of the time scales associated with wind power from hourly to seasonal fluctuations and how combining spatially distributed wind power sources helps to reduce its volatility. The analysis, based on observed wind speeds, is then generalised in a simple statistical model to develop a tool which can estimate the power output profile from a particular consortium of wind power sources. As the estimator only uses the local, or the mean national, wind resource and the mean distance between the sites to estimate the joint power output profile, it can be used by developers to estimate the reliability of their joint power output and to form the most effective consortium.

Social capital has become a highly successful concept in social science despite widely perceived shortcomings in conceptualization and operationalization. The latter is frequently performed as a principal component analysis of individual survey data with subsequent aggregation to regional or national levels. The central focus of this paper is the interpretation of the diverging correlations observed between the dimensions elaborated on an individual and an aggregate level. We illustrate that the correlations of regionally aggregated components are the result of an improper application of a single-level model to a multilevel structure. This mechanism is demonstrated empirically by adopting results from the European Social Survey and elaborating dimensions of social capital from both individual and aggregate survey data for European regions. The findings clearly indicate that the observed ecological correlations are not simply spurious or inconsistent due to an ecological fallacy condition, but rather reflect the influence of regional driving forces. Researchers need to be more careful in taking account of the multilevel nature of the data in order to produce valid results. In fact, the often applied procedure of individual factorization and subsequent aggregation of data provides a mixture of the two level effects with potentially misleading implications. [ABSTRACT FROM AUTHOR]