• Energy Research
• 2016-2025close
• 1. No poverty close

This dataset contains the underlying data for the following publication: Li, R., Perdana, S., Vielle, M. (2021), Potential integration of Chinese and European emissions trading market: welfare distribution analysis, Mitigation and Adaptation Strategies for Global Change, 26:22 https://doi.org/10.1007/s11027-021-09960-7.

Data for: Banking Stability, Natural Disasters, and State Fragility: Time Series Evidence for Developing Countries

The pandemic caused by Covid-19 has tested the resilience of public institutions, already burdened by a deep and complex crisis (political, economic, managerial). This crisis has revealed a discrepancy between the needs expressed by the community and the solutions adopted to satisfy them. This has been accompanied by a progressive worsening of decision-making efficiency and weak implementation capacity in a context of increasing environmental uncertainty. It is in local institutions, in particular, that the greatest problems are revealed, because of many endemic negative factors: political fragmentation, reduced economic resources, new forms of poverty. Against the background of this scenario, our study aims to analyze the reaction of local institutions to the pandemic crisis by looking at both welfare and communication services. The objective is to identify key features in understanding the resilience of municipalities. In other words, their ability to react and adapt to change, which is essential not only to deal with emergencies, such as the pandemic, but also to make the institution itself sustainable. Our interest is focused on a specific dimension of the resilience of the municipalities, related to collaboration with the third sector. The pandemic has shown that the continuous activism of non-profit organizations has allowed for the continuation of many so-called "ordinary" services, as well as the launch of several initiatives aimed at alleviating other social problems. The research has, first of all, an exploratory character that befits a new and still ongoing phenomenon. The basic questions concern the production of local welfare policies by municipalities. The data show different levels of "interventism" and different modes of communication. On this latter point, we observe the presence of significant attention-seeking among Mayors as community builders able, on the one hand, to reinforce the spirit of solidarity and, on the other, to uphold respect for the rules. On the services side, three main models of response to the pandemic emerge, two of which refer to the public-private relationship in local welfare policies. Findings suggest that these different reactions will have consequences in the immediate future for the management of the pandemic crisis (still ongoing). Specifically, the tendency is to employ a management of services based on partnership-model, which means that public-private collaboration is a pillar of local welfare. This seems to entail a greater legitimacy for individuals or associations to participate in the formulation and implementation of policies.

This Data Article accommodates secondary data, comprising of the available historic and current farm production data in Nigeria such as crop type, yields, prices, crop production inputs’ (e.g. labour, fertilizer, pesticide, seed, cash capital) as well as the Nigerian food consumption demand, and international commodity trade (import and export) data. The data were collected from reputable online databases such as FAOSTAT, World Bank, IMF, USDA, NBS, IITA, etc., and through personal research visits to Nigerian government agencies such as Federal and State Ministries of Agricultural and Rural Development, State Agricultural Development Agency (ADP) in Nigeria, National (Nigerian) Bureau of Statistics (NBS) and others; and were assembled, screened, processed (tabulated them according to GAMS table requirements). The data were applied via General Algebraic Modelling System (GAMS) to calibrate the ever first developed Nigerian Agricultural Sector Model, regionalised into existing six geo-political zones of Nigeria, in order to develop a reliable empirical tool for evidence-based agricultural development planning and policymaking in Nigeria. It is the Base (or Calibration) model, upon which the Nigerian Energy-Food Model (NEFM) is further built and applied to analyse the Nigerian Biofuels policy (i.e. bioethanol production potential), and its impacts on the Nigerian food and energy securities, job creation, rural economy development, and the entire Nigerian economy. Therefore, NASM-Data share some data with Mendeley Data (NEFM), while they both retain some data that are unique to them. Ndukwe Agbai Dick collected all the data, processed and applied them through GAMS to first develop NASM model that calibrates exactly the existing crop production environment and/or data in Nigeria before extending the model further to develop the NEFM, and finally applied the NEFM to analyse the Nigerian Biofuels Potential during his PhD research studies at Newcastle University, England, between October 2010 and September, 2014. The research visit took place between March 1st and June, 2012. Research findings from the analysis of these data using GAMS are currently being documented in paper titled "The Nigerian Agricultural Sector Model (NASM): A Sectoral Agricultural Policymaking Tool & An Empirical Model for Optimizing Food Production and Boosting Food Security in Nigeria", for submission to an international peer-reviewed and reputable high impact journal – Agricultural Systems (AS).

This dataset makes use of the UNEP (2018) report, Legal Limits on Single-Use Plastics and Microplastics: A Global Review of National Laws and Regulations, to create tabulated data categorising the type of legislation on single-use plastic bags enacted in 190 countries, updated to March 2019. Categories are Restrictions (ban), Levy/tax, New legislation proposed, Extended Producer Responsibility, Disposal Regulations, No national law found. For countries with bans on single-use plastic bags, full vs partial bans have been indicated. The dataset also draws on plastic waste estimations published in an article by Jambeck et al (2015), which has informed both UNEP 2018 reports on single-use plastics. Finally the dataset includes three indices of the Human Development Index 2019: Employment in Agriculture, Foreign Direct Investment, and Exports/Imports. Analyses of this dataset sought to understand the factors that influenced the type of single-use plastic bag policy a country implements. The supplementary dataset from Jambeck et al shows that middle-income countries (though the economy of each country has been updated in this dataset to reflect the 2019 status) have higher levels of plastic waste than their counterparts. However, ANOVA sets show that it is low-income countries that are most likely to have the most stringent legislations (restrictions and levy/tax), while middle-income countries are more likely to have obscure and largely ungoverned legislation (namely disposal regulation).

{"references": ["Hannah Ritchie, Max Roser, Edouard Mathieu, Bobbie Macdonald and Pablo Rosado - Data on CO2 and Greenhouse Gas Emissions by Our World in Data https://github.com/owid/co2-data#data-on-co2-and-greenhouse-gas-emissions-by-our-world-in-data", "Our World in Data, Cumulative CO2 emissions, 2020 https://ourworldindata.org/grapher/cumulative-co-emissions", "GDP, PPP (constant 2017 international $) - World Bank, International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme", "Cumulative CO2 Emissions of International Transport \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7114110", "Dataset Cumulative CO2 Emissions of International Transport \u2013 Joseph Nowarski, DOI:10.5281/zenodo.7114087", "Dataset Global Warming Forecast using Acceleration Factors - Joseph Nowarski, DOI:10.5281/zenodo.7151890", "CO2 Emission per GDP Forecast 2020-2100 - Joseph Nowarski, DOI:10.5281/zenodo.7264413"]} The dataset includes Business as Usual (BAU) forecast of world global CO2 emissions per GDP (Cp$) for 2020-2100. The CO2 emission forecast is from the publication “Dataset Global Warming Forecast using Acceleration Factors” [6]. According to this publication, the CO2 emissions without international transport will change from 33,803 MtCO2/y in 2020 to 70,191 MtCO2/y in 2100, a 108% increase. The GDP forecast applies a parabolic trendline of the last 30 years. According to this calculation, the world GDP will change from 126.3 MM$/y in 2020 to 728.1 MM$/y in 2100, a 476% increase. CO2 emissions per GDP (Cp$) are calculated by dividing the CO2 emissions per year by the GDP in the same year. The world 0.000268 tCO2/$GDP Cp$ in 2020 will decrease by 64% in 2100 to 0.000096 tCO2/$GDP.

The SDG Indicator 7.1.1: Access to Electricity, 2023 Release data set, part of the Sustainable Development Goal Indicators (SDGI) collection, measures the proportion of the population with access to electricity for a given statistical area. UN SDG 7 is "ensure access to affordable, reliable, sustainable and modern energy for all". Tracking SDG 7: The Energy Progress Report estimated that in 2019, 759 million people around the world lacked access to electricity. Moreover, due to current policies and the detrimental effects of the COVID-19 crisis, it is predicted that by 2030, 660 million people will still not have access to electricity, with a majority of these people residing in Sub-Saharan Africa. As one measure of progress towards SDG 7, the UN agreed upon SDG indicator 7.1.1. The indicator was computed as the proportion of WorldPop gridded population located within illuminated areas defined by annual VIIRS Nighttime Lights Version 2 (VNL V2) data. The SDG indicator 7.1.1 data set provides estimates for the proportion of population with access to electricity for 206 countries and 45,979 level 2 subnational units. The data set is available at both national and level 2 subnational resolutions.

Dataset for the paper titled Energy Poverty, Health and Education outcomes: evidence from the developing world

Yearly data [2000 - 2014] on carbon dioxide emissions, renewable energy consumption, fossil fuel energy consumption, total energy consumption, gross domestic product, population, and gross fixed capital formation for panel of 39 countries in Africa. It includes dataset on renewable energy consumption, total energy consumption, and GDP projections from 2015 to 2030 for all panels.