GDP Projections Research Articles

Projected Increase in Heatwaves under 1.5 and 2.0 °C Warming Levels Will Increase the Socio-Economic Exposure across China by the Late 21st Century

The impending challenge posed by escalating heatwave events due to projected global warming scenarios of 1.5 and 2.0 °C underscores the critical need for a comprehensive understanding of their impact on human health and socio-economic realms. This study delves into the anticipated implications of elevated global temperatures, specifically the 1.5 and 2.0 °C warming scenarios under the SSP2-4.5 and SSP5-8.5 pathways, on population and GDP exposure to heatwaves in China. We also evaluated the aggregated impacts of climate, population, and GDP and their interactions on future socio-economic exposure across China. We leveraged data sourced from the climatic output of Coupled Model Intercomparison Project Phase 6 (CMIP6) for heatwave analysis and integrated population and GDP projections under divergent Shared Socio-economic Pathways (SSPs), including SSP2-4.5 (low emission) and SSP5-8.5 (high-emission). Results indicate a drastic surge in the number of heatwave days under both warming scenarios, particularly in regions like Xinjiang (XJ), North China (NC), and South China (SC) subregions, with a notable disparity in the elevation of heatwave days among different levels. There is an alarming surge in population exposure, escalating approximately 7.94–8.70 times under the 1.5 °C warming scenario and markedly increasing by 14.48–14.75 times by the 2100s relative to the baseline (1985–2014) under the more extreme 2.0 °C warming level. Likewise, the study unveils a substantial elevation in GDP exposure, ranging from 40.65 to 47.21 times under the 1.5 °C warming level and surging dramatically by 110.85–113.99 times under the 2.0 °C warming level. Further analyses disclose that the climate effect predominantly influences changes in population exposure, constituting 72.55–79.10% of the total change. Meanwhile, the interaction effect notably shapes GDP exposure alterations, contributing 77.70–85.99% to the total change. The comprehensive investigation into alterations in population and GDP exposure under varying warming scenarios, coupled with the quantification of each contributing factor, holds paramount importance in mitigating the detrimental repercussions of heatwaves on both human life and socio-economic landscapes.

Can the improvements in human capital quality mitigate the negative impact of ageing on growth? Evidence from selected EU countries

AbstractMost long‐term GDP projections in developed countries expect a substantial decline in GDP growth rates over time. The main factor behind it is demographic changes, especially the decreasing working‐age population. We argue that these projections do not consider improvements in the quality of the human capital, which may, at least to some extent, mitigate the effects of the negative demographic shock. We start with a simple observation—the skills of younger age cohorts are higher than of older cohorts. Assuming that the current level of skills will be obtained also by the generations which will enter the labour market in the future, the average level of skills among the working‐age population will increase. Trying to catch this effect, we use the Programme for the International Assessment of Adult Competencies standardised test results to create an aggregate human capital measure, the average skill level of the work force, for 18 European Union countries and project it until 2040. We show that on average at least one sixth of the negative impact of the shrinking of the working‐age population can be offset by the increase in the quality of human capital and that this number can increase to three quarters in a less conservative scenario.

City-level population projection for China under different pathways from 2010 to 2100

Cities play a fundamental role in policy decision-making processes, necessitating the availability of city-level population projections to better understand future population dynamics and facilitate research across various domains, including urban planning, shrinking cities, GHG emission projections, GDP projections, disaster risk mitigation, and public health risk assessment. However, the current absence of city-level population projections for China is a significant gap in knowledge. Moreover, aggregating grid-level projections to the city level introduces substantial errors of approximately 30%, leading to discrepancies with actual population trends. The unique circumstances of China, characterized by comprehensive poverty reduction, compulsory education policies, and carbon neutrality goals, render scenarios like SSP4(Shared Socioeconomic Pathways) and SSP5 less applicable. To address the aforementioned limitations, this study made three key enhancements, which significantly refines and augments our previous investigation. Firstly, we refined the model, incorporating granular demographic data at the city level. Secondly, we redesigned the migration module to consider both regional and city-level population attractiveness. Lastly, we explored diverse fertility and migration scenarios.

The global economic impacts of the COVID-19 pandemic

The COVID-19 global pandemic has caused significant global economic and social disruption. We use global data on cases and deaths, and public health and economic policy responses to the pandemic, to illustrate the alternative past and potential future trajectories of the pandemic. Shocks to labor and sectoral productivity, consumption, government expenditure, and country and sector risk premia are used within a global multisectoral intertemporal general equilibrium model: G-Cubed, to assess the economic impacts of COVID-19 under those scenarios. We illustrate the vital role of public health responses in managing a pandemic and restoring confidence among economic agents. The role of global coordination amidst a pandemic is also highlighted. We also compare the GDP projections under the alternative scenarios with the actual GDP outcomes in 2020 and 2021. Despite the uncertainties regarding the health outcomes of COVID-19 and the health and economic policy responses to the pandemic, we demonstrate that the actual outcomes lie within those projected for the scenarios.

SSP economic growth projections: Major changes of key drivers in integrated assessment modelling

GDP scenarios are major drivers of climate change and climate change mitigation assessment studies. In this paper, a major update of the SSP GDP projections is presented. By using the most recent economic data and short-term projections by the World Bank and International Monetary Fund, the update captures changes in the system of national accounting and purchasing power parities, as well as the impact of the Covid 19 pandemic. Harmonization between the data and the original end-of-the century SSP projections was carried out in terms of GDP per capita in order to preserve the underlying narrative of income convergence. The result is a set of projections compatible with the most recent data and the SSP narratives. A comparison of DICE models calibrated to the original and updated SSP2 GDP per capita projections illustrates how significant the impact of an update of income data on integrated assessment results can be. The estimated global social costs of carbon in 2015 and 2030 rose by almost 30%.

Warming-induced increase in power demand and CO2 emissions in Qatar and the Middle East

Rising global temperatures in the Arabian Peninsula region caused by climate change have increased the demand for air conditioning, resulting in more electricity consumption and CO2 emissions. This paper treats Qatar as a representative country for understanding the effect of future regional warming on the electricity demand and CO2 emissions We first develop a model that relates daily electricity demand with temperature. By combining this model with temperature projections from the CMIP6 database (bias adjusted and statistically downscaled) and population and GDP projections from four shared socioeconomic pathways (SSPs), we can calculate Qatar's demand for electricity until the end of the century. The model identifies an average sensitivity of +4.2%/°C for the electricity demand and projects an increase in electricity demand by 5–35% due to warming alone at the end of this century. The model suggests that under SSP1-2.6, warming-induced CO2 emissions could be offset by carbon intensity improvements. Furthermore, under SSP5-8.5, assuming no carbon intensity improvement, future warming could add 20–35% of CO2 emissions per year by the end of the century, with half of the electricity demand related to more frequent hot days. We further found that the temperature effect on power demand and CO2 emissions is small compared to the effects from socioeconomic factors such as population, GDP, and carbon intensity.

Global gridded GDP data set consistent with the shared socioeconomic pathways

The vulnerability, exposure and resilience of socioeconomic activities to future climate extremes call for high-resolution gridded GDP in climate change adaptation and mitigation research. While global socioeconomic projections are provided mainly at the national level, and downscaling approaches using nighttime light (NTL) images or gridded population data can increase the uncertainty due to limitations. Therefore, we adopt an NTL-population-based approach, which exhibits higher accuracy in socioeconomic disaggregation. Gross regional product of over 800 provinces, which covering over 60% of the global land surface and accounted for more than 80% of GDP in 2005, were used as input. We present a first set of comparable spatially explicit global gridded GDP projections with fine spatial resolutions of 30 arc-seconds and 0.25 arc-degrees for the historical period of 2005 and for 2030–2100 at 10-year intervals under the five SSPs, accounting for the two-child policy in China. This gridded GDP projection dataset can broaden the applicability of GDP data, the availability of which is necessary for socioeconomic and climate change research.

On the impact of the Covid-19 health crisis on GDP forecasting: An empirical approach

<p style='text-indent:20px;'>Statistical dependence between the GDP growth projection adjustments for the end of 2020 and the health impact of the Covid-19 pandemic is detected and quantified for a broad set of countries. A <inline-formula><tex-math id="M1">\begin{document}$\texttt{rate }$\end{document}</tex-math></inline-formula> that captures this health impact for each country is contrasted to the difference in GDP growth projections for the end of 2020 released in two subsequent times: 2019 (pre-pandemic) and early 2020 (post-pandemic). The difference of this two variables exhibited a significant rank correlation with the <inline-formula><tex-math id="M2">\begin{document}$\texttt{rate }$\end{document}</tex-math></inline-formula>, and a linear model was successfully fitted, concluding that at the beginning of the pandemic health conditions played a significant role in the GDP projections.</p>

Changes in global heat waves and its socioeconomic exposure in a warmer future

Heat waves are continuous high temperature processes, which are defined as the daily maximum temperature exceeding the absolute-relative combined high temperature thresholds for more than three consecutive days in this study. Because of its unprecedented casualties, devastating compound disasters and irreversible deterioration trends, heat waves have attracted worldwide concern, while its global changes and socioeconomic impacts still need further study. Using three historical reanalysis data and multi scenario CMIP6 modeled data, Global Heat Wave Toolbox (GHWT) was developed to generate heat wave matrix from 1971 to 2100. The long-term changing characteristics of global heat waves were also analyzed. Next, population and GDP projections are employed to estimate future socioeconomic exposure and risks. The results show that except for high latitudes (latitude greater than 60°), high elevations and some coastal areas, heat waves have visited about 45% (±3%) of the global land area (excluding Antarctica). North Africa, North Australia, South Asia, and the Arabian Peninsula are detected as high heat wave area, where experience more than three heat waves with total duration over 15 days annually. Additionally, the average growth rate of global heat waves under SSP2 4.5 and SSP5 8.5 may be 2–3 times and 3–5 times of that under SSP1 2.6, respectively. Under SSP2 4.5, there may be about 7.32 billion people exposed to heat waves in 2100, accounting for 82.65% of the global population. Economic exposure may reach $433.37 trillion in 2100, accounting for 82.23% of the total global economy in that year. India could have the highest population and economic exposure, follow by populous countries, such as Pakistan and Bangladesh, and major economies, such as China and the United States. Under SSP1 2.6, the increase in heat waves may slow down significantly. Moreover, North Africa, Australia, and Brazil are identified as rapid heat wave growth area. Our study highlights the heat wave of future growth and its socioeconomic impacts, as well as the necessity for climate mitigation and adaptation measures.

Update of Ssp GDP Projections: Capturing Recent Changes in National Accounting, PPP Conversion and Covid 19 Impacts

Update of Ssp GDP Projections: Capturing Recent Changes in National Accounting, PPP Conversion and Covid 19 Impacts

Evaluating the Eurosystem/ECB staff macroeconomic projections: The first 20 years

AbstractThis work marks a thorough analysis of a confidential real‐time dataset consisting of the Eurosystem/ECB staff macroeconomic projections since their existence. By applying techniques widely employed in the literature of forecast evaluation, we examine their statistical properties with a special emphasis on optimality and rationality. Long‐term GDP projections are biased (tendency to overpredict), do not fully account for available information, and are outperformed by private sector expectations. Inflation projections are optimal and rational on a full‐sample analysis; however, subsample analysis reveals two distinct periods with a persistent and significant bias. Before the financial crisis inflation was persistently underpredicted, while in post‐2013, the bias reverses into overprediction.

Projection of the Effects of the COVID-19 Pandemic on the Welfare of Remittance-Dependent Households in the Philippines

The Coronavirus Disease 2019 (COVID-19) is inevitably affecting remittance-dependent countries through economic downturns in the destination countries, and restrictions on travel and sending remittances to their home country. We explore the potential impacts of the COVID-19 pandemic on the welfare of remittance-dependent households using a dataset collected in the Philippines prior to the outbreak. First, we confirm that remittances are associated with welfare of households, particularly for those whose head is male or lower educated. Then, we use the revision of the 2020 GDP projections before and after the COVID-19 crisis to gauge potential impacts on households caused by the pandemic. We find that remittance inflow will decrease by 14–20% and household spending per capita will decline by 1–2% (food expenditure per capita by 2–3%) in one year as a result of the pandemic.

The macroeconomic projections of the German government: A comparison to an independent forecasting institution

AbstractThis paper investigates the macroeconomic projections of the German government since the 1970s and compares it to those of the Joint Economic Forecast, which is an independent forecasting institution in Germany. Our results indicate that both nominal GDP projections are upward biased for longer forecast horizons, which seems to be driven by a false assessment of the decline in Germany’s trend growth and a systematic failure to correctly anticipate recessions. Furthermore, we show that the German government deviates from the projections of the Joint Economic Forecast, which in fact worsened the forecast accuracy. Finally, we find evidence that these deviations are driven by political motives.

Forecasting the GDP of a small open developing economy: an application of FAVAR models

ABSTRACTGDP forecasting remains a challenge for a small open developing economy. Faced with insufficient and low-frequency data, central bank forecasters cannot project GDP reliably for the purpose of monetary policy decision-making. An attempt is made to forecast GDP using a factor-augmented vector autoregressive (FAVAR) model for a small open developing economy. The forecasting accuracy of the FAVAR model is examined through sequential forecasts and benchmarked against a Bayesian vector autoregressive (BVAR) model. The main finding of this study is that a FAVAR model can generate consistent GDP projections for a small open developing economy despite data inadequacy.

G-RDEM: A GTAP-Based Recursive Dynamic CGE Model for Long-Term Baseline Generation and Analysis

We motivate and detail the newly developed G-RDEM recursive-dynamic Computable General Equilibrium model as a tool for long-term counterfactual analysis and baseline generation from given GDP and population projections. It encompasses an AIDADS demand system with non-linear Engel curves, debt accumulation from foreign saving and introduces sector specific productivity changes, endogenous aggregate saving rates, as well as time-varying input-output coefficients. Parameters for these relationships are econometrically estimated or taken from published work. The core of the model is derived from the GTAP standard model and seamlessly incorporated into the modular and flexible CGEBox modelling platform. Accordingly, it can be applied with various other extensions such as GTAP-AEZ, GTAP-Water or a regional breakdown for Europe to 280 NUTS2 regions. G-RDEM maintains the flexible aggregation from the GTAP data base. It is open source, encoded in GAMS and can be steered by a Graphical User Interface, which also encompasses a tool to analyse results with tables, graphs and maps. Existing GDP and population projections for the Socio-Economic Pathways 1-5 can be directly incorporated for baseline construction. A comparison of the generated long-term structural composition of the economy against a simple recursive-dynamic variant, using the basic CDE demand system of the standard GTAP model, uniform productivity growth, fixed saving rates and technology parameters, and no debt accumulation shows that G-RDEM brings about much more plausible results, as well as a more realistic, internally consistent representation of the economic structure in a hypothetical future.

The Macroeconomic Projections of the German Government: A Comparison to an Independent Forecasting Institution

This paper investigates the macroeconomic projections of the German government since the 1970s and compares it those of the Joint Economic Forecast, which is an in-dependent forecasting institution in Germany. Our results indicate that nominal GDP projections are upward biased for longer forecast horizons, which seems to be driven by a false assessment of the decline in Germany’s trend growth and a systematic failure to correctly anticipate recessions. We show that the German government also deviates from the projections of the Joint Economic Forecast, which in fact worsened the forecast accuracy. Finally, we find evidence that these deviations are driven by political motives.

Will the Sustainable Development Goals be fulfilled? Assessing present and future global poverty

Monitoring progress towards the fulfillment of the Sustainable Development Goals (SDGs) requires the assessment of potential future trends in poverty. This paper presents an econometric tool that provides a methodological framework to carry out projections of poverty rates worldwide and aims at assessing absolute poverty changes at the global level under different scenarios. The model combines country-specific historical estimates of the distribution of income, using Beta–Lorenz curves, with projections of population changes by age and education attainment level, as well as GDP projections to provide the first set of internally consistent poverty projections for all countries of the world. Making use of demographic and economic projections developed in the context of the Intergovernmental Panel on Climate Change’s Shared Socioeconomic Pathways, we create poverty paths by country up to the year 2030. The differences implied by different global scenarios span worldwide poverty rates ranging from 4.5% (around 375 million persons) to almost 6% (over 500 million persons) by the end of our projection period. The largest differences in poverty headcount and poverty rates across scenarios appear for Sub-Saharan Africa, where the projections for the most optimistic scenario imply over 300 million individuals living in extreme poverty in 2030. The results of the comparison of poverty scenarios point towards the difficulty of fulfilling the first goal of the SDGs unless further development policy efforts are enacted.

G-RDEM: A GTAP-Based Recursive Dynamic CGE Model for Long-Term Baseline Generation and Analysis

We motivate and detail the newly developed G-RDEM recursive-dynamic Computable General Equilibrium model as a tool for long-term counterfactual analysis and baseline generation from given GDP and population projections. It encompasses an AIDADS demand system with non-linear Engel curves, debt accumulation from foreign saving and introduces sector specific productivity changes, endogenous aggregate saving rates, as well as time-varying input-output coefficients. Parameters for these relationships are econometrically estimated or taken from published work. The core of the model is derived from the GTAP standard model and seamlessly incorporated into the modular and flexible CGEBox modelling platform. Accordingly, it can be applied with various other extensions such as GTAP-AEZ, GTAP-Water or a regional breakdown for Europe to 280 NUTS2 regions. G-RDEM maintains the flexible aggregation from the GTAP data base. It is open source, encoded in GAMS and can be steered by a Graphical User Interface, which also encompasses a tool to analyse results with tables, graphs and maps. Existing GDP and population projections for the Socio-Economic Pathways 1-5 can be directly incorporated for baseline construction. A comparison of the generated long-term structural composition of the economy against a simple recursive-dynamic variant, using the basic CDE demand system of the standard GTAP model, uniform productivity growth, fixed saving rates and technology parameters, and no debt accumulation shows that G-RDEM brings about much more plausible results, as well as a more realistic, internally consistent representation of the economic structure in a hypothetical future.

Sharing the emission reduction burden in an uneven world

In the climate agreement reached in Paris on the 12th of December 2015, the participating countries agreed to provide information about their Intended Nationally Determined Contribution, and also to assess its fairness and ambition. This study contributes a transparent empirical econometric tool for such an assessment. It shows that, using a formal decomposition framework that uniquely disentangles the macroeconomic and other influences on emissions, effort sharing in global greenhouse gas reductions can be achieved in a way that promotes innovation and environmental efficiency to reduce emissions without interfering with the right of poor countries to catch up economically. Based on GDP projections by the IMF and the OECD in a sample of about 185 countries, it presents country level CO2 emission targets applicable as benchmarks to assess the adequacy and fairness of national contributions.

Future growth patterns of world regions – A GDP scenario approach

Abstract Global GDP projections for the 21st century are needed for the exploration of long-term global environmental problems, in particular climate change. Greenhouse gas emissions as well as climate change mitigation and adaption capacities strongly depend on growth of per capita income. However, long-term economic projections are highly uncertain. This paper provides five new long-term economic scenarios as part of the newly developed shared socio-economic pathways (SSPs) which represent a set of widely diverging narratives. A method of GDP scenario building is presented that is based on assumptions about technological progress, and human and physical capital formation as major drivers of long-term GDP per capita growth. The impact of these drivers differs significantly between different shared socio-economic pathways and is traced back to the underlying narratives and the associated population and education scenarios. In a highly fragmented world, technological and knowledge spillovers are low. Hence, the growth impact of technological progress and human capital is comparatively low, and per capita income diverges between world regions. These factors play a much larger role in globalization scenarios, leading to higher economic growth and stronger convergence between world regions. At the global average, per capita GDP is projected to grow annually in a range between 1.0% (SSP3) and 2.8% (SSP5) from 2010 to 2100. While this covers a large portion of variety in future global economic growth projections, plausible lower and higher growth projections may still be conceivable. The GDP projections are put into the context of historic patterns of economic growth (stylized facts), and their sensitivity to key assumptions is explored.