Aggregate Capital Stock Estimations for 122 Countries: An Update

Berlemann, Michael; Wesselhöft, Jan-Erik

doi:10.1515/roe-2017-0004

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…As a measure for the output Y, the total value-added of a country, GDP, is used. An exception was a measure of physical capital stocks, which was taken from the compilation of Berlemann and Wesselhöft (2017). As a result, the authors provide the only available capital stock indicator being consistent over countries and uniformly covering the long-term panel dimension of almost 30 years.…”

Section: Datamentioning

confidence: 99%

Estimating the GDP effect of Open Source Software and its complementarities with R&D and patents: evidence and policy implications

Blind

Schubert

2023

J Technol Transf

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Open Source Software (OSS) has become an increasingly important knowledge asset in modern economies. However, the economic impact of OSS on countries’ GDP is ambivalent due to its public good character. Using a cross-country panel from 2000 to 2018, including 25 of the largest EU countries plus the USA, Japan, Korea, Canada, China, Norway, and Switzerland, matching OSS commits to GitHub to macroeconomic data provided by the OECD, our results confirm the dual nature of OSS. On the one hand, the open-access character creates great learning potential by providing a commonly accessible productive resource for all countries. On the other hand, it creates outward-directed spillovers associated with own OSS contributions. Accordingly, on average, we find that countries experience an increase in GDP when the world stock of OSS grows. However, smaller countries experience a decline in GDP resulting from their own contributions due to knowledge spillovers. The net effect is nonetheless positive. If no country contributed to OSS development, GDP for the average country would be 2.2% lower in the long run. Moreover, the losses associated with unintended spillovers are lower for countries with a higher R&D and patenting intensity. Based on our findings, we derive implications for policies and regulations concerning OSS.

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Section: Datamentioning

confidence: 99%

Estimating the GDP effect of Open Source Software and its complementarities with R&D and patents: evidence and policy implications

Blind

Schubert

2023

J Technol Transf

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“…Following the approach taken in , we then multiply PPP GDP by these NFW-to-GDP ratios to acquire proxies of physical capital. For Cuba, we use the ratio of Cuban and the US capital stock values from Berlemann and Wesselhöft (2017) and multiply this ratio with the US capital stock values from PWT 10.0. For North Korea, we multiply the capital-to-GDP ratio in Pyo and Kim (2020) with PPP GDP.…”

Section: Physical Capitalmentioning

confidence: 99%

DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise

Depsky¹,

Bolliger²,

Allen³

et al. 2023

Geosci. Model Dev.

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Abstract. Sea level rise (SLR) may impose substantial economic costs to coastal communities worldwide, but characterizing its global impact remains challenging because SLR costs depend heavily on natural characteristics and human investments at each location – including topography, the spatial distribution of assets, and local adaptation decisions. To date, several impact models have been developed to estimate the global costs of SLR. Yet, the limited availability of open-source and modular platforms that easily ingest up-to-date socioeconomic and physical data sources restricts the ability of existing systems to incorporate new insights transparently. In this paper, we present a modular, open-source platform designed to address this need, providing end-to-end transparency from global input data to a scalable least-cost optimization framework that estimates adaptation and net SLR costs for nearly 10 000 global coastline segments and administrative regions. Our approach accounts both for uncertainty in the magnitude of global mean sea level (g.m.s.l.) rise and spatial variability in local relative sea level rise. Using this platform, we evaluate costs across 230 possible socioeconomic and SLR trajectories in the 21st century. According to the latest Intergovernmental Panel on Climate Change Assessment Report (AR6), g.m.s.l. is likely to rise during the 21st century by 0.40–0.69 m if late-century warming reaches 2 ∘C and by 0.58–0.91 m with 4 ∘C of warming (Fox-Kemper et al., 2021). With no forward-looking adaptation, we estimate that annual costs of sea level rise associated with a 2 ∘C scenario will likely fall between USD 1.2 and 4.0 trillion (0.1 % and 1.2 % of GDP, respectively) by 2100, depending on socioeconomic and sea level rise trajectories. Cost-effective, proactive adaptation would provide substantial benefits, lowering these values to between USD 110 and USD 530 billion (0.02 and 0.06 %) under an optimal adaptation scenario. For the likely SLR trajectories associated with 4 ∘C warming, these costs range from USD 3.1 to 6.9 trillion (0.3 % and 2.0 %) with no forward-looking adaptation and USD 200 billion to USD 750 billion (0.04 % to 0.09 %) under optimal adaptation. The Intergovernmental Panel on Climate Change (IPCC) notes that deeply uncertain physical processes like marine ice cliff instability could drive substantially higher global sea level rise, potentially approaching 2.0 m by 2100 in very high emission scenarios. Accordingly, we also model the impacts of 1.5 and 2.0 m g.m.s.l. rises by 2100; the associated annual cost estimates range from USD 11.2 to 30.6 trillion (1.2 % and 7.6 %) under no forward-looking adaptation and USD 420 billion to 1.5 trillion (0.08 % to 0.20 %) under optimal adaptation. Our modeling platform used to generate these estimates is publicly available in an effort to spur research collaboration and support decision-making, with segment-level physical and socioeconomic input characteristics provided at https://doi.org/10.5281/zenodo.7693868 (Bolliger et al., 2023a) and model results at https://doi.org/10.5281/zenodo.7693869 (Bolliger et al., 2023b).

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“…In Equation ( 9), K-Actual capital stock of a certain year (priced at the constant price of the base year), I-Investment amount of fixed assets in a certain year (expressed at current price), P-Fixed asset investment price index based on a certain year, and δ t -Depreciation rate of a certain year [22][23][24].…”

Section: Index Selection and Data Description 231 Estimation Of Capit...mentioning

confidence: 99%

“…First, the output elasticity of labor, capital, and CO 2 obtained above are taken into Equation (23), and each year's total factor productivity level is calculated. The HP filtering method in the EViews 10.0 software is used to calculate the value of trend total factor productivity [37].…”

Section: Estimation Of Potential Outputsmentioning

confidence: 99%

Analysis of the Impact of Carbon Emission Control on Urban Economic Indicators based on the Concept of Green Economy under Sustainable Development

Xie

Li²,

Cao

2023

Sustainability

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With the deepening of the concept of sustainable development, a green economy has become the primary goal of urban development. Therefore, to improve the sustainability and far-reaching development of the urban economy, this work first discusses the concept of sustainable development. Second, the concept of a “green economy” is discussed. Lastly, based on the concept of green economic development, this work studies carbon emissions in Hebei Province, China, and discusses the impact of carbon emissions on the urban economy. On this basis, the impact of carbon intensity and CO2 emissions on economic growth is analyzed by establishing an endogenous growth model and state-space model, thereby revealing the importance of carbon emissions to the economic development of Hebei Province. In the analysis process, sensitivity analysis and a robustness test are also used to verify the reliability and robustness of the model results. Finally, this work summarizes the research conclusions and puts forward relevant policy suggestions, which provide a reference for developing a green economy in Hebei Province. The results reveal that from 1999 to 2020, the average output elasticity of labor, capital, and CO2 in Hebei Province are 0.4002, 0.3057, and 0.2941, respectively. This shows that carbon emissions are essential to Hebei’s economic growth. In other words, Hebei’s economic development mainly depends on enterprises with high carbon emissions. Additionally, in the optimistic case, Hebei’s potential output growth rate will show a downward trend, but will soon rise. This indicates that even under strict carbon emission control, Hebei’s economic growth rate will still pick up based on the support of high-tech. This work not only provides a reference for the development of Hebei’s green economic system, but also contributes to the sustainable development of the urban economy in the future.

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Aggregate Capital Stock Estimations for 122 Countries: An Update

Cited by 10 publications

References 8 publications

Estimating the GDP effect of Open Source Software and its complementarities with R&D and patents: evidence and policy implications

Estimating the GDP effect of Open Source Software and its complementarities with R&D and patents: evidence and policy implications

DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise

Analysis of the Impact of Carbon Emission Control on Urban Economic Indicators based on the Concept of Green Economy under Sustainable Development

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