Economic Growth Scenarios Research Articles

Evaluation and Prediction of Carbon Storage in the Qinghai-Tibet Plateau by Coupling the GMMOP and PLUS Models

Land-use alterations exert a profound impact on carbon storage within terrestrial ecosystems. Exploring the spatiotemporal dynamics of regional land use and carbon storage is crucial for optimizing national spatial planning and fostering low-carbon development. For this study, we utilized land-use data spanning from 2000 to 2020 for the Tibetan Plateau and assessed the spatial and temporal variations in carbon storage using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. We adjusted the carbon density within the provinces in the study area as a prerequisite. Moreover, we integrated the Grey Multi-objective Decision-making (GMMOP) model with the Patch-generating Land-use Simulation (PLUS) model to forecast carbon storage alterations in 2030 across various scenarios. The findings indicated that between 2000 and 2020, the overall carbon storage witnessed a decrease of 18.94 × 108 t. Carbon storage in grassland decreased by 22.10 × 108 t, and carbon storage in unused land, forest land, cultivated land, construction land, and water increased by 1.56 × 108 t, 0.92 × 108 t, 0.66 × 108 t, 158.50 × 104 t and 26.74 × 104 t, respectively. The soil organic carbon pool exhibited the highest average carbon storage of 195.63 × 108 t, whereas the litterfall organic carbon pool contained the lowest average carbon stock of 15.07 × 108 t. In comparison with the levels observed in 2020, the total carbon storage experienced a reduction of 8.66 × 108 t and 5.29 × 108 t under the inherent progression and economic growth scenarios, respectively. Conversely, it rose by 11.87 × 108 t and 16.21 × 108 t under the environmental preservation and holistic progression scenarios, respectively. Under the holistic progression scenario, the belowground biomass organic carbon pool exhibited the highest carbon storage increase of 5.59%. These findings offer valuable insights for the management and enhancement of carbon sinks in the Qinghai-Tibet Plateau.

Is carbon emission trading policy a panacea? The implications of promoting green total factor productivity

Market‐based environmental regulation is crucial in influencing environmental and socioeconomic development. Carbon emission trading (CET) policy in China has been implemented since 2012 to balance the environment and economy, especially the green total factor productivity (GTFP). Most previous studies have only conducted short‐term investigations on the impact of CET on environmental and socioeconomic development. In this study, we investigated panel data from 30 regions in China during 2003–2019, adopted the Global Malmquist‐Luenberger and Super‐SBM methods to measure GTFP, and employed the propensity score matched difference in difference (PSM‐DID) to investigate the effect of CET policy on the environment, GTFP, and secondary industry output. The results indicate that CET policy has a positive influence on carbon emission reduction, contributes to the improvement of GTFP, and provides economic value. Therefore, we believe that CET policy can effectively help China to achieve a win‐win scenario of environmental protection and economic growth.

Forecasting of technology innovation and economic growth in Indonesia

This article explored the scenarios of future technology innovation and economic growth. The feedback economics' perspective was applied in forecasting future technology innovation and economic growth. The system dynamics modelling of economic growth by incorporating technology innovation revealed that: i. General Purpose Technologies (GPTs) investment should be linked with industrial policy as a leverage point; ii. The post-COVID-19 industrial policy should diffuse GPTs for inclusive growth and sustainable development, it is different with the Indonesian pre-COVID-19's industrial policy, specifically before the 1997 economic crisis; iii. Rethinking the Indonesian industrial policy is to rejuvenate the existing industrial-based GPTs' complex, and the inter-ministerial policy coordination is a challenge to implement the industrial-based GPTs policy instrument. Putting the GPTs' innovation into practice by using industrial policy for future economic growth sustainability is the unique insight on reindustrialization. The scientific contributions of this study were: i. on theoretical basis, the framework and model enriched the feedback economics' view of Cavana et al. and; ii. on innovation economics' studies, the framework and model support the concepts of Lundvall. Finally, future work from this study is designing different feedback structure that could generate different results.

Macroeconomic challenges for Brazil post-covid-19

The objective of this article is to comprehensively investigate the adverse effects resulting from the persistent high interest rate policy in Brazil over many years. These effects have directly impacted the servicing of public debt, bringing significant negative consequences to social well-being. This situation is exacerbated by the influence of these factors on the productive sector, notably the industry, as well as on the physical, social, and technological infrastructure of the country. The concerning reality is that public debt already accounts for over 50% of public expenses, which has a pronounced impact on various key aspects of society. Therefore, it is imperative to create space for a broad and informed debate about the long-term implications of high interest rates and their influence on public debt management. Only through careful reconsideration of these policies and a strategic reshaping of financial priorities will it be possible to steer Brazil towards a scenario of sustainable economic growth and lasting social well-being.

Ecological well-being and its equity in various economic contexts: Evidence from 130 cities in the yangtze river economic belt

Economic development often leads to environmental damage, resulting in the disorder of ecological well-being in various regions and affecting the equity of ecological well-being in multiple areas. However, economic growth and ecological well-being do not always have a single linear relationship. Therefore, exploring the changes in ecological well-being and its equity in different economic growth contexts is a question worthy of study, which will be beneficial to achieving high-quality development. This paper constructs an analytical framework to explore the equilibrium point between economic growth rate and ecological well-being and its equity. It explores the state of ecological well-being and its equity under different economic growth rates by simulating the monetary value of ecological well-being in 130 cities in the Yangtze River Economic Belt under different economic growth scenarios. The results show that: ① The SD-PLUS model has high simulation accuracy and can simulate land-use change and ecological well-being in different scenarios. ② By 2035, the overall ecological well-being and GDP growth will show an inverse U-shaped relationship. Under the six growth rates measured in this article, a 5% GDP growth rate will be closest to the inflection point. ③ By 2035, there will be a U-shaped relationship between ecological well-being equity and GDP growth. Under the six growth rates measured in this article, a 5% GDP growth rate will be closest to the inflection point. Therefore, when formulating policies, the rate of economic development should be planned rationally and kept above 5% as far as possible so that when ecological well-being per capita is relatively consistent, rapid economic development can be maintained while improving the equity of ecological well-being and achieving high-quality development.

Achieving Chinaʼs carbon neutrality goal by economic growth rate adjustment and low-carbon energy structure

To effectively address global warming, all countries need to achieve carbon neutral targets as soon as possible, and it is of great importance when China, as the world's largest emitter, achieves its carbon peak and subsequently its carbon neutral target. To examine these twin goals of achieving a carbon peak and carbon neutrality, we comprehensively apply the component data method, the cointegration analysis method and the scenario analysis method. By presenting 27 scenarios of achieving carbon neutrality on the basis of first achieving a carbon peak through economic growth rate adjustment and energy consumption structure, the results show that under low economic growth and low-carbon energy consumption scenarios, China's carbon emissions will peak in 2026, and will achieve the carbon neutrality target in 2056. However, under other scenarios, the carbon peak is only reached in 2030, and China cannot achieve carbon neutrality by 2060. Therefore, China needs to accept slower economic growth and achieve a cleaner net-zero energy consumption structure.

Long-term electricity generation analysis and policy implications – the case of Ghana

The pursuit of a cost-effective and low-carbon electricity generation environment is critical to achieving Ghana's economic and industrial ambitions. Ghana’s development agenda calls for an average electricity consumption of about 5,000 kWh per capita by 2030. To this end, the effective harnessing of energy resources requires the implementation of robust policies for sustainable electricity generation. This study employs the IAEA MESSAGE analytical tool to conduct a quantitative assessment of electricity generation in Ghana from 2020 to 2048. The findings show that, by 2048, a diversified electricity generation scenario will result in a 32.30% decrease in cost and a 55.27% reduction in CO2 emissions, compared to an accelerated economic growth (AEG) scenario, which will increase cost and CO2 emissions by 12.21% and 21.10%, respectively. The results underscore the importance of ensuring that electricity generation policies balance economic, environmental, and social concerns. Achieving a green energy transition agenda in Ghana and other developing nations will require a long-term commitment to a generation mix that is both sustainable and economically viable. The implementation of such a policy will require an informed and dedicated effort from all stakeholders.

On the adoption of stricter energy efficiency standards for residential air conditioners: Case study Guayaquil, Ecuador

Space cooling is the fastest-growing energy end-use in buildings worldwide, and Ecuador is no exception. Nevertheless, the last update of the Minimum Energy Performance Standards (MEPS) for air conditioners was in 2013 (EER 3.2W/W); since then, no new standards have been proposed in Ecuador. This study is the first assessment of stricter MEPS and estimation of benefits for the consumers and society of the residential sector in Guayaquil, Ecuador. The life cycle cost, payback time, net present value, electricity savings, and CO2 mitigation are the outputs from the Policy Analysis Modeling System (PAMS) methodology followed. The analysis considers future economic scenarios until 2035. Also, a new engineering approach based on linear optimization defines ACs designs in compliance with the proposed MEPS at the lowest cost. Therefore we can avoid setting less ambitious energy-efficiency targets when efficiency options are limited in the market (this is the Ecuadorian case). The analyzed MEPS are those proposed by UNEP and by the renewal program of inefficient equipment of the Ecuadorian Government. Our estimates show that AC demand can reach 17.3% of the total residential electricity demand in the business-as-usual scenario and 21.4% in the high economic growth scenario until 2035.Furthermore, the results show a significant gap between the proposed MEPS which can be progressively bridged. The best standard from the consumer perspective is EER 4.3W/W, while from the societal perspective, it is EER 5.5W/W. Stricter MEPS can reduce AC electricity demand and energy-related emissions between 5.7% and 31%, depending on the selected scenario. Therefore, stricter MEPS for AC represents a cost-effective option to reduce energy needs and emissions from air conditioners and a concrete action to support the national energy efficiency policies and nationally determined contributions for the residential sector.

Influencing factors and scenario forecasting of carbon emissions in Liaoning Province, China

Liaoning is a province with large energy consumption and carbon emissions. Management of carbon emissions in Liaoning Province is crucial to realizing China's carbon peaking and carbon neutrality goals. To clarify the driving factors and trends of carbon emissions in Liaoning Province, we analyzed the impacts of six factors on carbon emissions in Liaoning Province through STIRPAT model based on carbon emission data from 1999 to 2019. The impact factors included population, urbanization rate, per-capita GDP, secondary industry ratio, energy consumption per unit GDP, and coal consumption ratio. Nine forecasting scenarios with three economic and population growth models and three emission reduction models were set up, and their carbon emission trends under the above nine forecasting scenarios were predicted. The results showed that the main driving factor of carbon emissions in Liaoning Province was per-capita GDP, and that the main inhibitor was energy consumption per unit GDP. The carbon peak year in Liaoning Province would fluctuate between 2020 and 2055 under the nine forecasting scenarios, with peak values ranging from 544 to 1088 million tons CO2. The medium economic development growth and high carbon emission reduction scenario would be the optimal carbon emission scenario in Liaoning Province. Under this forecasting scenario, Liaoning Province could achieve carbon peak (611 million tons CO2) by 2030 without affec-ting economic development through optimizing energy structure and controlling the intensity of energy consumption. Our results would be helpful for seeking the best path for carbon emission reduction in Liaoning Province and providing a reference for its realization of carbon peaking and carbon neutrality goals.

Environmental Governance Goals of Local Governments and Technological Innovation of Enterprises under Green Performance Assessment.

The current study empirically estimates the impact of local government environmental governance on enterprise technological innovation from the perspective of a green political performance assessment of local governments with Chinese characteristics. Fourteen years of data (from 2006 to 2019) on pollutant emissions, and the patents of A-share listed companies were collected from 230 cities in China. A fixed effect model and tool variable method were applied to empirically analyze the objectives of the study. The results show that the environmental governance formulated by the local government has regional differences, which are shown as lower governance indicators for underdeveloped areas and higher governance indicators for developed areas. Environmental governance has a greater promotion effect on technological innovation in enterprises in developed regions, as well as in large and private enterprises. Moreover, mechanism analysis showed that the local governments preferred the path of financial subsidies to promote the level of technological innovation in enterprises. This study provides a foundation for attaining the "win-win" scenario of local government environmental stewardship and high-quality green economic growth.

Post-COVID-19 economic growth scenarios for Mexico City

Post-COVID-19 economic growth scenarios for Mexico City

An optimal integrated power and water supply planning model considering Water-Energy-Emission nexus

Water and energy are the major factors in the new United Nations Sustainable Development Goals. Sustainable conversion and management in the secure supply of increasing demand for water and electricity is a challenging concern. It is recently complicated due to economic development, population growth, resource depletion, and climate change, which requires a simultaneous progressive planning. Since energy and water systems are instinctively interdependent and mutually interacted (known as nexus), this paper employed an integrated solution-based method. A bottom-up optimization model is developed considering Water-Energy-Emission Nexus approach for both power and water sectors. The model minimizes the economic costs subjected to techno-economic constraints and socio-environmental requirements. An innovative multi-dimensional index called Water-Electricity Nexus Index is presented to assess how water and power sectors are interdependent. The model is applicable for any region and the results for a real case show that the nexus index is increasing remarkably (7.6,3.3,47.7 changes to 12.3,3.7,55.0) if the current situation continues for both sectors in the Business-As-Usual scenario. It means the two sectors are interweaving more in the coming years, which will make planning and security provisions difficult. The nexus of electricity for water sector increases by about 60 % and although the share of water withdrawal for power sector is 3.7 % in the region, the total water withdrawal is much more (55 % over the life cycle considering virtual water). Hence, several scenarios are defined to depict future transition pathways and optimal strategies considering demands, prices, taxation policy, restrictions, and technological efficiency. The results show that rapid economic growth scenarios are not sustainable transition pathways (i.e. the nexus index changes to 16.1,20.8,112.0 and 16.1,20.3,47.7). Finally, it is concluded that increasing the renewable resource of power generation capacity between 22 and 40 %, improvement of the achievable technological efficiency in power plants from 25 to 50 percent, as well as demand-side management to keep the annual growth rate of electricity and water between 1.5 and 2.5 % and 1.1–1.5 %, imposing the carbon tax (5–75 $ton) and water tax (0.1–2 $m3) are the major drivers to reduce the nexus index. This framework provides significant insights to improve the integrated planning of water and power sectors in the regions for managers and authorities.

Renewable energy strategy analysis in relation to environmental pollution for BRICS, G7, and EU countries by using a machine learning framework and panel data analysis

The present research uses machine learning, panel data and time series prediction and forecasting techniques to establish a framework between a series of renewable energy and environmental pollution parameters, considering data for BRICS, G7, and EU countries, which can serve as a tool for optimizing the policy strategy in the sustainable energy production sector. The results indicates that XGBoost model for predicting the renewable energy production capacity reveals the highest feature importance among independent variables is associated with the gas consumption parameter in the case of G7, oil consumption for EU block and GHG emissions for BRICS, respectively. Furthermore, the generalized additive model (GAM) predictions for the EU block reveal the scenario of relatively constant renewable energy capacity if gas consumption increases, while oil consumption increases determine an increase in renewable energy capacity until a kick point, followed by a decrease. The GAM models for G7 revealed the scenario of an upward trend of renewable energy production capacity, as gas consumption increases and renewable energy production capacity decreases while oil consumption increases. In the case of the BRICS geopolitical block, the prediction scenario reveals that, in time, an increase in gas consumption generates an increase in renewable energy production capacity. The PCA emphasizes that renewable energy production capacity and GHG, respectively CO2 emissions, are highly correlated and are integrated into the first component, which explains more than 60% of the variance. The resulting models represent a good prediction capacity and reveal specific peculiarities for each analyzed geopolitical block. The prediction models conclude that the EU economic growth scenario is based on fossil fuel energy sources during the first development stage, followed by a shift to renewable energy sources once it reaches a kick point, during the second development stage. The decrease in renewable energy production capacity when oil consumption increases indicates that fossil fuels are in trend within the G7 economy. In the case of BRICS, it is assumed that gas consumption appears because of increasing the industrial capacity, followed by the increase of economic sustainability, respectively. In addition, the generalized additive models emphasize evolution scenarios with different peculiarities, specific for each analyzed geopolitical block.

Multi-objective energy planning for China's dual carbon goals

Long-term dependence on fossil fuels for economic growth is a primary driver of carbon emissions in emerging economies such as China. To achieve China's dual carbon goals (DCGs) of carbon peaking and carbon neutrality, we developed a dynamic input-output multi-objective optimisation model, combined with scenario setting, to explore the optimization pathways of carbon emissions, economic growth, and energy consumption. With reference to policy reports, we set different economic growth scenarios, and consider ambitious, moderate, and conservative carbon emission scenarios. The results showed that large-scale expansion of the tertiary industry is essential to realize the DCGs. The cumulative carbon emissions for the ambitious scenario were approximately 1.8 times higher than those for the conservative scenario. Further, the carbon intensity in 2030 would decrease by >69.2 % compared to the 2005 level. It means that China can achieve carbon reduction targets for 2030. We projected that future Chinese energy consumption will peak in 2035, reaching 180.64 EJ. We also optimized the installed electricity generation mix to be consistent with future development scenarios for China. This study provides valuable insights into the strategies and policy requirements to meet China's newly announced DCGs to assist in mitigating global climate change.

Assessing the contribution of optimizing energy mix to China's carbon peaking.

To cope with climate change, China commits that it will strive to achieve carbon peaking by 2030. Using the Cointegration model and the Markov Chain model, this paper forecasts China's carbon emissions during 2019-2030 in six scenarios, and assesses the contribution of optimizing the energy mix to China's carbon peaking. The research obtains three main conclusions. Firstly, optimizing the energy mix will contribute to achieving China's carbon peaking. In the economic slow-growth scenario, taking China's planned target of energy mix (PTEM) into account, the carbon peaking year will be brought forward from 2028 to 2023. In the economic medium-speed-growth scenario, optimizing the energy mix will make China achieve carbon peaking in 2028. Without considering the PTEM, however, the carbon emissions will not peak before 2030. In the economic fast-growth scenario, the peaking year will not occur whether considering the PTEM or not, but the growth rate of carbon emissions with the PTEM will be far lower than that without considering the PTEM. Secondly, in all three economic growth scenarios, optimizing the energy mix will largely reduce the growth rate of carbon emissions, and thus significantly reduce the peak value of carbon emissions. Thirdly, optimizing the energy mix has a negative adjusting effect on the impact of economic growth on the growth rate of carbon emissions, and the negative effect rise as the economic growth rate increases.

Інтенсивне економічне зростання: можливості та наслідки

This paper is devoted to the study of the features and conditions of intensive economic growth in the context of Ukrainian realities. The research tool was a simulation model based on tables “cost-output”, developed at the Institute of Economics and Forecasting of the National Academy of Sciences of Ukraine. In the course of the research a variant analysis of macroeconomic aggregates was carried out to identify specific effects of the financial aspects of the implementation of scenarios of intensive economic growth were considered and an example was given that illustrates the benefits of intensive growth. The analysis of calculations clearly demonstrates the effect of the Keynesian investment multiplier in intensive growth. Research has also revealed the effect of passive gross investment savings, which is not recorded in scientific publications. It has been experimentally proven that due to these effects, intensive economic growth becomes not only possible but also profitable.

Gearing Urban Metabolism toward the Carbon Neutrality Target: A Case Study of Hebei Province, China

Urban metabolism has emerged over the past decades as an important new paradigm of regional and urban sustainability governance towards a Chinese national scheme of ‘carbon neutrality’ by 2060. Hebei province in China faces twin pressures related to its supply of water and energy resources, which has brought humans and nature into conflict. Overcoming this tension in the human-land relationship in Hebei and determining a suitable development path for the future has become a core issue for the achievement of coordinated development within the Beijing–Tianjin–Hebei region. This paper constructs a system to simulate the metabolism of water, energy, and human relationships, and uses this model to carry out simulations for Hebei province. The model establishes five scenarios: a natural development scenario, economic growth scenario, water conservation development scenario, energy conservation development scenario, and low carbon scenario. The simulation results show that, without intervention, the natural development scenario results in greater pressure on supply gaps and a greater demand for water and energy, with more production of industrial waste gas and domestic wastewater discharges. The economic growth, water conservation development, and energy conservation development scenarios focus on single economic, water conservation, and energy conservation measures by looking at core economic, water, and energy elements within the metabolic system; however, solving issues with individual elements merely leads to other, remaining problems. Under the low carbon scenario, issues with multiple elements in Hebei’s metabolic system are considered more comprehensively, so the simulation results are better than those in the other scenarios, and it better fits the future orientation of sustainable development of Hebei province.

Scenario Analysis of Energy-Related CO2 Emissions from Current Policies: A Case Study of Guangdong Province

Regional carbon management is essential for China to achieve the carbon reduction target. Studying the emission peak time and volume for Guangdong Province, which is the largest economic province with rapid carbon growth, is important for developing regions to formulate low-carbon policies. In this study, an end-use energy-based emission model integrating the guidelines for provincial CO2 emission peaking action plans and updated policies was developed to study the regional energy-related peaking time and emission scale. Taking Guangdong as a case, multiple scenarios were designed and analyzed considering factors of economic development, energy intensity, and structure. The results show that the energy-related CO2 cannot reach its peak by 2030 without control, even under low economic growth scenarios. Specifically, under high economic growth scenarios, the carbon peak can only be addressed by 2030 at around 0.65 Gt in the context of a strong transformation in energy structure as well as improvement in energy efficiency. Under medium and low economic scenarios, energy-related CO2 emissions will reach a plateau before 2030 between 0.61 and 0.64 Gt by implementing the medium and strengthening optimization of energy structure. Thus, effective measures are necessary for Guangdong’s peak target in both energy efficiency improvement and energy structure optimization. At last, this study puts forward policy recommendations for the low-carbon pathway of Guangdong Province, which can provide an experience for other regions in understanding their carbon emission trajectories and help policymakers enact appropriate actions.

Energy Demand Projection and Economy Nexus of Pakistan

In the fundamental study to design sustainable electricity generation, long-term electricity demand forecasting has taken on a crucial role. The Low Emissions Analysis Platform (LEAP®) software is used in this study to estimate the demand for energy by various consumer groups including commercial, residential, agricultural, and industrial for the study period (2023-2050). The electricity demand is forecasted under different Gross Domestic Product (GDP) growth scenarios. The developed scenarios are reference, low economic growth, medium economic growth, and high economic growth scenarios. This study identifies the key relationship between energy demand and the economy of Pakistan. It is found that energy demand and the GDP of the country have a direct relationship as one quantity increases, the other one also increases, and vice versa. 3.56% growth rate of GDP under the low GDP growth scenario causes an increment in energy demand from 137.97 million MWh in 2023 to 374.79 million MWh. Medium and high GDP growth scenario pertains to GDP growth rates of 5.79% and 10.22% which causes an increment in energy demand to 652.17 million MWh and 1,244.75 million MWh by 2050. The results of this study are significant and provide insight into electricity demand growth under the economic scenarios that could be useful for sustainable electricity planning in Pakistan.

Water demand forecasting and countermeasures across the Yellow River basin: Analysis from the perspective of water resources carrying capacity

Study regionThe Yellow River Basin plays a crucial role in China's socio-economic development and ecological security. Study focusThe growing trend of water consumption driven by anthropogenic activities has posed an increasing number of challenges for the sustainable development of the Yellow River basin. Water demand prediction plays a central part in facilitating water conservation by developing water allocation strategies. Hence, the water demand prediction model based on the concept of water resources carrying capacity was built to predict water demand with different future scenarios and provide reference strategies for water allocation. New hydrological insights for the regionWith all changing scenarios under consideration, the future demand for water resources will significantly increase compared with the current conditions. The range of water demand by each province along the Yellow River basin is exhibited in this study. Two scenarios with different economic development conditions and cascading water use efficiency are investigated. In addition, solutions for improving water use efficiency and increasing the available quantity of water are put forward to ameliorate water stress. Specifically, water distribution schemes with a medium level of water use efficiency in different economic growth scenarios are proposed based on the available water resources from the Yellow River basin and the South-to-North Water Diversion Project. This work will benefit local planners and decision-makers.