Population growth and climate change: A dynamic integrated climate-economy-demography model

Lupi, Veronica; Marsiglio, Simone

doi:10.1016/j.ecolecon.2021.107011

Cited by 19 publications

(8 citation statements)

References 42 publications

(65 reference statements)

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“…We can gain insights into the nexus between the variables under study from such an enriched environmental Kuznets curve relationship. Our results can be cast under the demographic transition setting (Lupi & Marsiglio, 2021) and confirm the usefulness of using panel data to explain long term phenomena and to measures of environmental degradation. We find an N‐shaped curve with turning points at

\approx

6.506 and 129.05 millions.…”

Section: Resultssupporting

confidence: 74%

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

Maruotti

Loro

2023

Environmetrics

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We introduce a bivariate bidimensional mixed‐effects regression model, motivated by the analysis of CO2$$ {\mathrm{CO}}_2 $$ emission levels and growth on OECD countries from 1990 to 2018. The model is able to capture heterogeneity across countries and allows for a full association structure among outcomes, assuming a discrete distribution for the random terms with a possibly different number of support points in each univariate profile. We test the behavior of the proposed approach via a simulation study, considering several factors such as the number of observed units, times, and levels of heterogeneity in the data. Empirically, we define an extended version of the STIRPAT model where all model parameters, and not only the mean, vary according to a regression model. Our empirical findings provide evidence of heterogeneous behaviors across countries and suggest the need of a flexible approach to properly reflect the heterogeneity in both the emission levels and the growth processes.

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\approx

6.506 and 129.05 millions.…”

Section: Resultssupporting

confidence: 74%

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

Maruotti

Loro

2023

Environmetrics

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“…For instance, Cheshire and Magrini 12 conducted an analysis of the population in 12 metropolitan areas within the European Union, revealing that cities with more favorable weather experienced higher rates of population growth. Similarly, Lupi and Marsiglio 13 discovered that climate change can influence birth and mortality rates. Regarding terrain and population, Feng et al 14 conducted an analysis and provided evidence of a strong logarithmic relationship between relief degree and population using GIS technology.…”

Section: Literature Reviewmentioning

confidence: 95%

Urban population prediction based on multi-objective lioness optimization algorithm and system dynamics model

Liu¹,

Yan²,

Wang³

2023

Sci Rep

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Population size is closely related to economic and social development and change. It is one of the primary and essential elements of overall urban development planning to formulate a population development strategy scientifically through population projections. Therefore, we propose an urban population prediction model based on a multi-objective lioness optimization algorithm and system dynamics. The multi-objective lioness optimization algorithm is used to optimize some critical parameters of the system dynamics model to reduce the subjectivity of the model construction. Taking Xi’an as an example, the validity of the model is verified, and the population size of Xi’an from 2019 to 2050 is predicted by the model. In addition, the impact of different policies and their combinations on the future population is discussed through simulations of three scenarios composed of five policy factors: birth, employment, science and technology, healthcare and education. The results show that the total population of Xi’an will peak at 147,939,242 in 2040, based on current development trends. Moreover, the five policies with the largest to smallest positive effect on population size are: employment policy, fertility policy, education policy, science and technology policy, and health policy, with employment and fertility policies having significantly larger effects than the other three. Therefore, the employment policy and the birth policy are the two most effective policies to promote population growth, and the coordinated implementation of the five policies is the fastest way to increase population size.

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“…From the perspective of total emissions, human activities result in city carbon emissions [ 75 ]. The number of people residing in cities also affects CO2 emissions and economic activities [ 76 , 77 ], and the growth of the urban population increases the total carbon emissions [ 56 ].Although an increase in urban population size results in more CO2 emissions overall [ 78 ], per-capita emissions have decreased due to the ability of urban population growth to advance technology, increase the effectiveness of using public facilities, and lower CO2 emissions [ 79 , 80 ]. According to Hong’s research the critical urban population size is one million [ 81 ].…”

Section: Research Review and Frameworkmentioning

confidence: 99%

What influences the performance of carbon emissions in China?—Research on the inter-provincial carbon emissions’ conditional configuration impacts

Chen,

Li,

Cai

et al. 2024

PLoS ONE

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The severe global warming issue currently threatens humans’ existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments’ configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government’s carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China’s eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region’s resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

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Population growth and climate change: A dynamic integrated climate-economy-demography model

Cited by 19 publications

References 42 publications

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

Urban population prediction based on multi-objective lioness optimization algorithm and system dynamics model

What influences the performance of carbon emissions in China?—Research on the inter-provincial carbon emissions’ conditional configuration impacts

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Population growth and climate change: A dynamic integrated climate-economy-demography model

Cited by 19 publications

References 42 publications

CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model

CO2 emissions and growth: A bivariate bidimensional mean‐variance random effects model

Urban population prediction based on multi-objective lioness optimization algorithm and system dynamics model

What influences the performance of carbon emissions in China?—Research on the inter-provincial carbon emissions’ conditional configuration impacts

Contact Info

Product

Resources

About

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model

CO₂ emissions and growth: A bivariate bidimensional mean‐variance random effects model