Improving smallholder poultry productivity to 2050 in

This dataset summarizes global population and GDP scenarios in 0.5 × 0.5 degree grids by country between 1980 and 2100 by 10 years. The data in 1980-2010 are estimated by downscaling actual populations and GDPs by country, while those in 2020-2100 are estimated by downscaling projected populations and GDPs under three shared socioeconomic pathways (SSP): SSP1; SSP2; and SSP3, by country. Global population and GDP projections for years 2020-2100 created by downscaling projected populations and GDPs under three shared socioeconomic pathways (SSP): SSP1; SSP2; and SSP3 (source: SSP database version 1) The dataset is modified Global dataset of gridded population and GDP scenarios, version 3 Decomposition of GDP per capita growth rate for USA and SSA-L in SSP2 and SSP3. With respect to labor, it is the quality adjustment due to education and the ratio between population growth and working population growth that matters most

Global dataset of gridded population and GDP scenarios

GitHub - Nowosad/global_population_and_gdp: Global dataset

Future growth patterns of world regions - A GDP scenario

GDP / Cap / Year Kcal / Cap / Day kcal_cap (105 countries, 1990/2000) kcal_cap (fitted values) kcal = 802 * gdp^(0.142327) [R^2 = 0.66] 0 2 4 6 8 10 12 14 16 1900 1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 Billion Low fertility, low mortality High fertility, high mortality Central fertility, central mortality (Lutz et al. 2001) Demograph OECD-based SSP2 scenario) and there would be a 1% annual GDP carbon intensity improvement. The latter assumption is consistent with historical global GDP carbon intensity reduction with an average of 1.5-1.6% for 1990-2016 period (Enerdata, 2017, US EIA, 2016b). Although GDP growth-based estimates are used for qualitative comparisons

SSP2. v9. 130325. I would like to calculate gdp and population annually by adding rows for each intermediate years and interpolating the GDP and population. I have grouped by country and used. complete (Year=full_seq (2010:2100,1)) %>% fill (Model, Scenario, Version, SSP, Date) to create the years and fill for variables that remain the same • GDP projections for 187 countries covering 2000- 2100 in five- year time steps for all five SSPs • Harmonized to IIASA population projections • Levels in $2005 at 2005 purchasing power parity (PPP) exchange rates. GDP data. 1 Groups: Country [177] Country Year Model Scenario population gdp.x SSP Version Date Series.Name Series.Code Country.Code gdp.y <chr> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> 1 Afghanistan 2010 OECD Env-Growth SSP2_v9_130325 28.0 0.0159 SSP2 v9 130325 NA NA NA NA 2 Afghanistan 2011 OECD Env-Growth SSP2_v9_130325 NA. Heating degree day Per capita GDP (2010) Left‐top box : Regional characteristics Left‐bottom box : Socio‐economic characteristics Right‐bottom box: Energy service demands Population (SSP2) GDP per capita (SSP2) Important to consider provincial characteristics when estimating service demands in the residentia

SSP Scenario Database - SSPs - IIAS

Global dataset of gridded population and GDP (1980-2010 estimations and 2020-2100 scenarios) - global_population_and_gdp/01_data_download.R at master · Nowosad. For the industry sector, we start from the lower value of the existing SSP1 and SSP2 trajectories but apply an additional GDP-per-capita-dependent factor to the rate of change of energy intensity GDP and Population growth projections for SSP2 from IIASA. GDP & poultry systems. As a country's Gross Domestic Product (GDP) increases, poultry production systems become more intense and commercial. The proportion of chickens raised by smallholder farmers in Nigeria is projected to reduce from 75% in 2010 to 20% by 2050

Because by 2050, the GDP of CYUA will be lower than that of the MYRUA, especially far lower than that of the YRDUA. The GDP under the SSP2 scenario will be 7.2 times (CNY 15.45 trillion), 9 times (CNY 27.66 trillion) and 10.5 times (CNY 78.63 trillion) larger in 2100 than it was in 2010 for the CYUA, MYRUA and YRDUA, respectively Here we follow the Shared Socioeconomic Pathways (SSPs 36) in our assumptions for GDP, population and inequality trends. Using the middle-of-the-road pathway SSP2, and in the absence of climate.

Explainer: How 'Shared Socioeconomic Pathways' explore

Joint GCAM Community Modeling Meeting and GTSP Technical Workshop Joint Global Change Research Institute College Park, Maryland, USA The SSP (Shared Socioeconomic Pathways) an The SSP2 GDP projection is thus situated in-between the estimates for SSP1 and SSP3, which reach 2100 global average income levels of 82 and 22 (thousand year-2005 USD/capita PPP), respectively. SSP2 depicts a future of global progress where developing countries achieve significan GDP per capita | SSP2 GDP per capita | SSP3 Population | SSP2 Population | SSP3 Billion people a d g j k h 100 Thousand 2005Int$ per capita Thousand 2005Int$ per capita 80 60 40 20 0 10 100 80 60 40 20 0 8 6 4 2 0 MJ per 2005Int$ gCO 2 per MJ 100 80 60 40 20 0 gCO 2 per MJ 100 80 60 40 20 0 gCO 2 per MJ 10 8 6 4 2 0 MJ per 2005Int$ 10 8 6 4 2 The years 2000-2016 are rescaled, with the aim to better match measured national GDP in these years. Rescaling for ISIMIP2a extension: In the original downscaling, 2000 was the last year where the input national time series were based on measured GDP. From 2010 onwards, SSP2-based national time-series were used as input

Shared Socioeconomic Pathways - Wikipedi

SSP Databas

  1. This study downscales the population and gross domestic product (GDP) scenarios given under Shared Socioeconomic Pathways (SSPs) into 0.5-degree grids. Our downscale approach has the following features. (i) It explicitly considers spatial and socioeconomic interactions among cities, (ii) it utilizes auxiliary variables, including road network and land cover, (iii) it endogenously estimates the.
  2. The only GDP growth projection in the SSP framework that departs from global economic convergence is the IIASA model's quantification of SSP3 (Cuaresma 2017). This is the pathway that comes closest to historical trends and could as such better be considered business as usual than the most pessimistic scenario
  3. ssp2 ‐iiasa ‐r32eu15 ssp2 ‐iiasa ‐r32eu12‐m SSP2 ‐PIK ‐R32EU15 SSP2 ‐PIK ‐R32EU12‐M SSP2 ‐OECD ‐R32EU15 SSP2 ‐OECD ‐R32EU12‐
  4. 10.1.2 Energy Intensity. The dynamic calibration of factor productivity of energy services (''tfpn'') is run based on the SSP2. The following income elasticity rule is used for the different regions: industrialized countries (OECD members) are characterized by an elasticity of 0.40 in 2005 whereas non-OECD members have an elasticity of 0.55 based on the higher share of energy expenditures
  5. al 4.5 W m −2 radiative forcing level by 2100 - approximately corresponding to the RCP-4.5 scenario. Thirdly, the SSP3-7.0 scenario is a medium-high reference scenario within the regional rivalry socio-economic family, while the final Tier 1.
  6. Gross Domestic Product (GDP) follows regional historical trends (Dellink et al., 2015). With global average income reaching about 60 (thousand year-2005 USD/capita, purchasing-power-parity - PPP, i.e., GDP/capita) by the end of the century, SSP2 sees an increase of global average income by a factor 6. The SSP2 GDP projectio

the same in 2050 as under SSP2. World GDP, per capita GDP and. population are specified as growing at 3.1 percent, 2.4 percent. and 0.68 percent per year respectively under both scenarios A continuous and consistent table of global GDP data (in 2005 PPP USD) for 195 countries based on the merged MPD and PWT8.1 data, extended using PWT9.1 and WDI data, and consistent with OECD SSP2 GDP projections starting in 2010

Input Data & Bias Correction - ISIMI

Shared socio-economic pathways and their implications for

The annual global GDP exposure increases between the base period and 2046-2065 by about 5.56-fold (RCP2.6-SSP1), 4.39-fold (RCP4.5-SSP2), and 3.35-fold (RCP8.5-SSP3). The GDP exposure is highest under the RCP2.6-SSP1 scenario, in which sustainable development proceeds moderately quickly, technological change is rapid (including reductions in. SSP2 Middle of the Road Source: Jiang, 2014, Pop & Env. SSPs Global Projection Results: GDP per capita year 0.5 5.5 10.5 15.5 20.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 1950 1970 1990 2010. Figure 2 shows GDP changes associated with changes in space heating and cooling demand at the global level in SSP2 compared to no climate change case. The GDP's negative impacts in 2100 are highest (median: −0.94%) in the 4 °C scenario, whereas the 1.5 °C scenario maintains a low GDP change (median: −0.05%)

GCAM v5.2 Documentation: GCAM Shared-Socioeconomic Pathway

Results of matched GDP per capita time series for Maddison

Accordingly, the SSP1 and SSP5 scenarios are assigned relatively near-term convergence years of 2125, while SSP3 and SSP4 scenarios are assigned 2200, and SSP2 is assigned an intermediate value of 2150. The downscaling method first calculates an emissions intensity, I, for the base and convergence years using emissions level, E, and GDP The next variable in the equation is GDP-per-head. Under SSP2 global per capita GDP rises from the current level of around 10,000 dollars to 60,000 dollars in twenty one hundred. Under SSP2, energy intensity falls from around 8 terajoule per dollar today to 2 terajoule per dollar in twenty one hundred Seventh, we adjust the estimate for the additional indirect CO 2 emissions from NSR-driven marginal economic growth in Europe and Asia from Bekkers et al. using SSP2 GDP projections and extrapolate it to non-CO 2 emissions from these regions under a given RCP scenario. Eighth, the RF and emissions as a result of NSR from steps 5, 6 and 7 are.

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SSP2: Middle of the Road consists of a world following a path in which social, economic, and technological trends follow historical patterns including growth inequality. And right now, there is a unique opportunity for countries, rich and poor, to make radical progress towards limiting the likelihood of catastrophic climate change History SSP2 Life expectancy 1970 1990 2010 2030 2050 rs 55 60 65 70 75 80 85 History SSP2 Prosperity/Equity 1970 1990 2010 2030 2050) 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 15 5 10 15 20 25 30 35 40 45 History SSP1 SSP2 Gini GDP/cap Source: Lutz et al. (2018), historic data from UNDESA (2017) However, the economic strategy of this scenario differs considerably than the SSP1 and SSP2, letting so GDP growth rates take their highest possible values. Innovation and investments are the most preferable options in SSP5, where technological progress and competitive markets drive growth We found that the estimated values of the technological parameter were positively correlated with the GDP. Using the estimated relationship, we predicted future crop yield during 2020 and 2100 under SSP1, SSP2 and SSP3 scenarios and RCP 2.6, 4.5, 6.0 and 8.5. The estimated crop yields were different among SSP scenarios

SSP2 and SSP 5. •In 2050, for SSP2 and SSP5 the supply is 80kt and 104kt whereas the demand is 99kt and 147kt respectively. •If the collection rate is 60%, then the gap increases further. 12 Co for EV US Demand 2020 2025 2030 2035 2040 2045 2050 SSP2-2.6 15.133908 27.65038 41.55061 56.24896 71.28436 86.06304 99.8489 SSP2 describes a scenario of continued growth of floor space per capita in all regions. Although there has been a large difference in per‐capita living space in countries at a similar stage of development, there has been a general trend of increasing floor‐space with growing GDP, followed by an eventual slowing of demand (OECD/IEA, 2017 )

Figure 2Input layers for the benefit-cost analyses: (a) sea-level rise for the RCP4.5 scenario in 2080, (b) subsidence in 2080, (c) change in GDP for the SSP2 scenario in 2080, and (d) current protection standards estimated with the FLOPROS modelling approach the GDP per capita in the absence of climate chan-ge (BAU_SSP2). Under this scenario the GDP per capita is expected to increase by factors of 4 to 7 (this range increases under different scenarios and higher spatial disaggregation), depending on the delta. By 2050, the GDP per capita in the Mahanadi Delta would reach around 11,000 USD/cap, aroun

projected as a function of GDP per capita . Historical feed conversion efficiencies [kg protein in product / kg protein in feed] SSPs: Drivers Quantification for GLOBIOM SSP2: MESSAGE-GLOBIOM (IIASA, International Figure 1 shows the GDP per capita and population developments, respectively. The storylines have lower population growth in SSP1 and SSP5, middle level of growth in SSP2 and SSP4 and a high level of population growth in SSP3. Economic growth is highest in SSP1 and lowest in SSP3. GDP developments are in general opposite to population developments gdpの数値には、各sspの人口見通しに加え、国際貿易の流れや技術開発など、各sspの叙述に沿った仮定が盛り込まれています。 gdp成長率が最も高いのはssp5で、各国の発展と収束が急速に進み、2100年の一人当たりの世界平均gdpは約14万ドルになります。一方. Table 20 presents the quantitative translation of the the storyline elements of SSP1, SSP2 and SSP3 in terms of electrification rate for industry and feedstocks. These indicators apply to 2010-2100; Intensity improvements are in FE/GDP annually (Fricko et al., 2017 [ 17 ] ) GDP in Netherland SSP1 SSP2 SSP3 SSP4 SSP5 0 5 10 15 20 25 30 2000 2025 2050 2075 2100 n) Population in Netherland SSP1 SSP2 SSP3 SSP4 SSP5 from OECD SSP Database 17 •Per capita food and processed products supply is aggregated for RI, WH, MZ, and SB, and the unit is changed from kg/year to g/day

Academia.edu is a platform for academics to share research papers Global demand for education: Global population younger than 15 until 2100, according to Wittgenstein Centre's SSP2 CER . Next Post These SMEs contribute to more than 55% of Global GDP & over 67% of Global Employment Global demand for education: Global population younger than 15 until 2100, according to Wittgenstein Centre's SSP2 FT . Next Post Population breakdown by highest level of education achieved for those aged 15+ These SMEs contribute to more than 55% of Global GDP & over 67% of Global Employment SSP2-NDC and SDP-1.5C scenarios are shown as bars, the 'intermediate' scenarios SSP1-NDC and SSP1-1.5C using symbols. (change in GDP). d, Inequality and poverty: prevalence of extreme poverty.

SSP1 SSP2 SSP3 SSP4 SSP5 Scenario M t / y r Scenario SSP1 SSP2 SSP3 SSP4 SSP5 Globally, more wheat is produced in SSP3 than an in SSP4 due to the high population and high waste. However, because of the USA's comparative advantage in this scenario, the USA produces more wheat in SSP4 than in SSP3. 205 SSP2: Middle of the Road. Crop yield developments projected as a function of GDP per capita . based on econometric estimation on 1980-2010, and 4 income group clusters. SSPs: Crop yield development (GLOBIOM) 12 Shared Socio-economic Pathways (SSPs) 06/12/2018 Regional population, under the SSP2 scenario 1970 1990 2010 2030 2050 0 5 10 15 20 25 30 thousand USD PPP 2005, per year pbl.nl Global GDP per capita, per scenario 1970 1990 2010 2030 2050 0 10 20 30 40 50 60 thousand USD PPP 2005, per year pbl.nl Regional GDP per capita, under the SSP2 scenario Japan and Oceania History SSP1 scenario SSP2. First, estimates from SSP2 are consistent with median estimates of the present forecasts. SSP2 is described as a middle of the road scenario, with medium demographics, development of advanced energy technologies, frontier productivity growth, and regional convergence. The present study focuses on gross domestic product (GDP) per. SSP2: Oliver Fricko, Petr Havlik, Joeri Rogelj, Zbigniew Klimont, Mykola Gusti, Nils Johnson, Peter Kolp, Manfred Strubegger, Hugo Valin, Markus Amann GDP projections by IIASA: Jesús Crespo Cuaresma, Income projections for climate change research: A framework based on human capital dynamics,.

GDP per capita SSP2 2010 2040 2070 2100 0 2 4 6 8 10 MJ per US$2005 Energy intensity SSP2 2010 2040 2070 2100 0 20 40 80 100 % Share of fossil fuels PES SSP2 2010 2040 2070 2100 0 10 20 30 40 50 60 % Share of low carbon tech PES SSP2 2010 2040 2070 2100 0 2 4 6 8 10 12 Billion people Population SSP3 2010 2040 2070 2100 0 20 40 60 8 (GDP, population, governance, education, technology) SSP1 SSP2 SSP3 SSP4 SSP5 g m 2) 2.6 4.5 6.0 8.5 Scenarios for impact analysis Climate Socio-economic reference pathways Main architecture new scenarios . 2000 2020 2040 2060 2080 2100 ) -20 0. impacts. (South Asia and India lose more than 4% of their GDP, Eastern Asia and Sub Saharan Africa roughly 2% of their GDP in 2050 in RCP 8.5). Introducing rigidities in market adjustments increases climate change costs (by roughly 30%), but does not change substantively the picture. Key is the modelling of catastrophic outcome Purpose was to present and discuss proposals for numerical scenario data on core parameters (Population, GDP and Urbanization), consistent with the draft narratives emerging from the Boulder meeting. The meeting was attended by researchers from the global IAM teams, and experts on the various topics discussed GDP per capita | SSP2 GDP per capita | SSP3 Population | SSP2 Population | SSP3 Billion people a d g j k h 100 Thousand 2005Int$ per capita Thousand 2005Int$ per capita 80 60 40 20 0 10 100 80 60 40 20 0 8 6 4 2 0 MJ per 2005US$ gCO 2 per MJ 100 80 60 40 20 0 gCO 2 per MJ 100 80 60 40 20 0 gCO 2 per MJ 10 8 6 4 2 0 MJ per 2005US$ 10 8 6 4 2

In the SSP2 scenario, global GDP is projected to continue to grow at a rate of around 2.8 per cent but decreases over the 21st century to about 1.9 per cent, which we use for the growth rate estimate in the calibration of the discount rate SSP2 is a business-as-usual scenario. SSP3 is a pessimistic scenario with higher population growth, lower GDP growth, and a lower rate of urbaniza-tion SSP2. To stay as close as possible to the storyline of the SSPs, the main trends and assumptions have been downscaled for each scenario in Table 1. For example, population growth in SSP2 is assumed to be moderated, while in the other two scenarios, it is assumed that the growth rate will be lower, indicating a gentler slope GDP M M M EDU M M M HIC MIC LIC POP L H H URB L L L GDP L L L EDU L L L HIC MIC LIC POP H L L URB H H H GDP H H H EDU H H H HIC MIC LIC POP M L L URB H H H GDP M/H M/H M/H SSP2 SSP3 SSP4 75 80 85 90 95 100 105 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 r Female life expectancy at birth SSP1 SSP2 SSP3 SSP4 0 500 1000 1500 2000 s Net. with GDP and population projections from the IMF World Economic Outlook of October 2019 and October 2020 (IMF, 2019, 2020). We use inequality Gini projections from Rao et al. (2018) for SSP2 as our near -term baseline for the Gini index. For the energy and environmental variables

GDP Gross Domestic Product GTAP Global Trade Analysis Project IAM Integrated Assessment Model IEA International Energy Agency IIASA International Institute for Applied Systems Analysis IIASA-SSP2 Projections for SSP2 by the IIASA modelling team IMF International Monetary Fund ILO International Labor Organizatio Economic growth (GDP per capita): Long-term economic growth rate: 1.5%/year. Near-term economic growth rate: 2.5%/year. Transition time to convert from near-term economic growth rate to long-term economic growth rate: 75 years. Energy intensity of GDP. Buildings and Industry: 1.2%/year improvement in energy efficienc World GDP ($2005 trillion) 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 0 100 200 300 400 500 600 700 800 900 1,000 1,100 Year SSP1 SSP2 SSP3 SSP4 SSP5 Dominiquevan derMensbrugghe (GTAP) SSPs&Asia 21-Mar-2016 22/3

All models were run with gross domestic product (GDP) and population values from Shared Socioeconomic Pathway 2 (SSP2) (21, 22). In SSP2, global population reaches 9.3 billion by 2050, an increase of 35% relative to 2010, and global GDP triples Gross Domestic Product (GDP) per capita Population and urbanisation projections France has been directly confronted by the question of a just transition through the (SSP2) and a 'Regional Rivalry' (SSP3) scenario. The shaded area delineates the G20 average in 2015 for easy reference SSP2-4.5 . Continuity with CMIP5 and interest from CORDEX/Decadal Prediction/DAMIP. SSP1-2.6. Continuity with CMIP5 and representative of low end of range. Tier 2. SSP1-6.0 . Continuity. SSP4-3.7 . Filling the Gap (interesting mitigation target less stringent than RCP2.6) Overshoot scenario. SSP3-7. ensemble enriching the exploration of. GDP per capita under SSP2 and SSP3, $2007 Dominique van der Mensbrugghe ESA/FAO Rome, 5-6 May 2014 4 0 10,000 . 20,000 . 30,000 . 40,000 . 50,000 . 60,000 . 70,000 . World Developing East Asia & Pacific South Asia Europe & Central Asia Middle East & North Africa Sub-Saharan Africa Latin America & Caribbean High-income 2010 2050—SSP2 2050. Future flood risk is estimated using population and GDP exposures consistent with the SSP2 and SSP3 scenarios. Population and GDP are defined as being affected by a flood when a grid cell receives any depth of flood inundation. The earthquake hazard is quantified using a 10,000-year stochastic catalog of over 15.8 million synthetic earthquake.

(PDF) Future growth patterns of world regions – A GDPSSPs 路径下实现全球可持续发展目标的可能性分析Material Flow Analysis (MFA): Metal Demand and Climate

r - Adding rows and interpolating values - Stack Overflo

Population (million people) GDP per capita (USD) Rural Urban GDP per Capita Figure 2. Weekly per capita food consumption, in percentage.Source: Kenya IHBS 2005/2006 cereals 41% starches 20% pulses 8% vegetables 6% meat 3% dairy and eggs 14% fish 3% other 5% Bottom quintile cereals 29% starches 16% pulses 7% vegetables 10% meat 6% dairy and eggs. models. e) Change in the rate of GDP/cap growth from the damage function. Ranges represent the uncertainty in the calibration of the damage function. a2) Country-level GDP per capita after climate change impacts without and with pulse of 1 GtC of CO2 in the atmosphere in 2020 (respectively, blue and green lines and ranges) log[GDP(US$)] 7 8 9 China United States Japan India Russia Brazil l Japan Canada Germany Republic of Korea Mexico Indonesia Saudi Arabia Fig. 4 | Winners and Losers of climate change among the g20 nations. Country-v GSCC ( , CSCC/GSCC) er 2013 CO2 . T CSCC o (ρ =2%, μ = 1.5) SSP2/RCP6. BHM-SR. P CSCC cor GDP 2015. D GSCC . R ea SSP2 is the inevitable business-as-usual scenario in which not much has changed from the present day. Some progress has been made in curtailing emissions and income inequality, but the pace of. SSP2 SSP4 (b) GDP per capita 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Constant − price MER GDP per capita, ratio with 2015 DECC Low DECC Mid DECC High (c) Oil price 0 50 100 150 200 250 300 Oil price, year 2015 dollars/bbl 2000 2010 2020 2030 2040 2050 2060 Year Level 1 Level 2 Level 3 (d) Carbon price 0 50 100 150 200 Carbon price, year 2015 dollars/tCO2.

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r - Applying function to specfic rows of given column

Effect of SSPs (for RECref): POP + GDP growth + = 13.2.2019 4 0 2000 4000 6000 8000 10000 12000 14000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 World population (Million) SSP1 SSP2 SSP3 SSP4 SSP5 0 100000 200000 300000 400000 500000 600000 700000 800000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 World GDP (Milloin $ 2005) SSP1. As shown, the GDP shares (solid lines) of Brazil and Mexico are 34% and 24% in 2010, respectively, and decline to 23% and 25% by 2100 in the SSP2 scenario. The shares of energy consumption, passenger kilometers travelled, and all other transportation-related quantity variables also follow the GDP trends in going from 2010 to 2100


Future economic growth assumptions are developed by first harmonizing per capita GDP growth rates with assumptions made by the Energy Information Administration's National Energy Modeling System model in the Annual Energy Outlook 2019. The justification to switch to the SSP2 dataset for our future population assumptions is two-fold SSP2: Middle of the Road. SSP3: Regional rivalry • Competition among regions • Low technology development • Environment and social goals not a priority • Focus on domestic resources • High population growth • Slow economic growth dev. countries. SSP4: Inequality. SSP5: Fossil fuel-ed development • Rapid growth, free trade • High. Under SSP1-2.6, we observed wetting trends over large areas of China except the arid region during 2020-2099; however, under SSP2-4.5, SSP3-7.0 and SSP5-8.5, significant drying trends are detected in the humid and temperate semi-humid region, while in other areas there are significant wetting trends 1 Supporting Online Materials 2 3 Spatially explicit global gross domestic product (GDP) data 4 set consistent with the Shared Socioeconomic Pathways 5 6 Tingting Wang, Fubao Sun* 7 8 9 Supporting figures 10 11 Figure S1 Comparison between national (a) and subnational (b) population between 12 official data against gridded data sets in 2005, values in the legend are thei Figure 7 includes the spatial distribution of changes for GDP and POP over HHH river basin due to 1.5°C and 2°C of warming based on the SSP2 scenario. Compared to present period (Figure 7a ), substantial growth of GDP (Figure 7c,e ) can be seen throughout HHH river basin under the 1.5°C and 2°C warmer targets, with the greater growth.

A sustainable development pathway for climate action

Gross Domestic Product (GDP) per capita Population & urbanisation projections Russia's economy is heavily reliant on fossil fuels, with the energy sector constituting between 20-23% of GDP, 25-26% of consolidated budget revenues and 55-60% of export revenues in recent years. Despite projections of a minimal increase in futur Agricultural gross domestic product across the country fell by 3.6 percent, while gross domestic product across all sectors in the drought-prone lowlands fell dramatically, by over 11 per cent (Koo et al. 2019). Note: SSP2 is Shared Socioeconomic Pathway 2 of the IPCC represents a middle of the road scenari

Improving smallholder poultry productivity to 2050 in

SSP2 is a middle-of-the-road projection, where social, economic and technological trends do not shift markedly from historical patterns, resulting in a U.S. population of 455 million people by 2100. Domestic migration trends remain consistent with the recent past, however output from the HadGEM2-ES climate model was used to dynamically update. SSP2 global total • 0.43% GDP loss in RCP8.5 • <0.1% loss in low emissions scenarios • The negative impact mainly comes from cooling demand increase Cooling demand increase Heating demand decrease Range indicates 5 GCM uncertaint Table 19 presents the quantitative translation of the the storyline elements of SSP1, SSP2 and SSP3 in terms of electrification rate for the residential and commercial sectors. These indicators apply to 2010-2100; Intensity improvements are in FE/GDP annually (Fricko et al., 2017 [ 17 ] ) • GDP per capita by . Productivity. region •Power plant conversion efficiency •Transport fuel economy, etc. •Crop yields, etc. Technology •Fossil fuel, uranium, solar, wind, geothermal, land, water and other. Resources •Pollution control •NDCs •Water use. Policies. INPUT (Quantified Assumptions) • Price of energy • Energy.

Sustainability Free Full-Text Population and Economic

A: Energy intensity is the amount of energy used to produce a unit of GDP. The economy becomes more energy efficient when it uses less energy. This improvement could be driven by technology or by a shift in the output mix. Thus, if energy intensity is decreasing (as in our scenarios), GDP is increasing and energy prices are falling SSP2 29 ,33 35 available from the GLOBIOM model. 36 According to the SSP2, the global popu- lation projectionsshow anincrease by35% in 2050relativeto 2010(to 9.3billion).Global GDP is assumed to grow more than threefold between 2010 and 2050, but stronger during the firs GLOBIOM SSP2 SSP2 Present 9 2.4 1.69 390 Medium Moderate GLOBIOM SSP3 SSP3 Present 12.7 1.6 3.47 390 Slow Constrained IMAGE SSP2_450_BECC S SSP2 RCP 2.6 9 2.4 1.69 490 Medium Moderate IMAGE SSP2_450_REF SSP2 RCP 2.6 9 2.4 1.69 490 Medium Moderate LUISA reference SSP2 SSP2 9 2.4 1.69 850 Medium Moderat SSP2—called the middle of the road scenario—is treated by many modeling groups as a business-as-usual scenario. Labor force growth is being generated by the GIDD projections (appendix A). The projections are available by broad age group (the 15-64 age cohort for the labor force is used here), gender, and education (primary, secondary, and. Updated the long term GDP per capita rate from 1%/year to 1.5%/year but achieved sooner, i.e., decreased convergence time. Changed the last GDP historic year from 2018 to 2019; it is now in $2017 PPP (purchasing power parity) instead of $2011 PPP

Socioeconomic Heterogeneity in Model Applications(PDF) Continuous national Gross Domestic Product (GDP