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India is a decisive player in the global economic platform and continues to hold its standing as the fastest-growing major economy. The rapid rise in population growth, the surge in vehicular traffic, and the establishment of small-scale industries are leading to a deteriorating environment, road accidents, worsening traffic congestion, and inequality in access to mobility in Indian cities. Better urban mobility can play a major role in unlocking the Indian cities’ potential. In this paper, a system dynamics-based sustainable urban transportation model is designed and developed to reduce total vehicle population, carbon emissions, and road traffic congestion by monitoring the impact of five formulated and generated policy scenario maps. The system dynamics approach is a computer-aided approach based on causal feedback structure and feedback dynamics. The model incorporates seven submodels: total population, gross domestic product, environmental impact, total vehicle population, road transport demand, road transport infrastructure, and road traffic congestion. The factor of policy scenario implication is selected as a control variable within the model simulation framework. The model simulation results indicate that a powerful endorsement policy scenario map can lead to a decrease in the total number of vehicles, which in turn promotes the rapid adoption of sustainable technologies, such as electric vehicles. This model also contributes to enhancing urban mobility in India by creating a sustainable urban transportation system and achieving carbon neutrality.

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of ‘IAMs’. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

Over the last decades, energy and pollution control policies combined with structural changes in the economy decoupled emission trends from economic growth, increasingly also in the developing world. It is found that effective implementation of the presently decided national pollution control regulations should allow further economic growth without major deterioration of ambient air quality, but will not be enough to reduce pollution levels in many world regions. A combination of ambitious policies focusing on pollution controls, energy and climate, agricultural production systems and addressing human consumption habits could drastically improve air quality throughout the world. By 2040, mean population exposure to PM2.5 from anthropogenic sources could be reduced by about 75% relative to 2015 and brought well below the WHO guideline in large areas of the world. While the implementation of the proposed technical measures is likely to be technically feasible in the future, the transformative changes of current practices will require strong political will, supported by a full appreciation of the multiple benefits. Improved air quality would avoid a large share of the current 3–9 million cases of premature deaths annually. At the same time, the measures that deliver clean air would also significantly reduce emissions of greenhouse gases and contribute to multiple UN sustainable development goals. This article is part of a discussion meeting issue ‘Air quality, past present and future’.

In many parts of the world, a rapid urbanization process is taking place at an unprecedented scale, and its drastic impacts on societies and the environment are evident. To combat the externalities of such rapid, and to a degree uncontrolled, development, many cities around the globe introduced various urban growth management policies. However, policy making—to provide sustainable outcomes, while generating growth opportunities—has been a daunting task for urban administrators. To ease the task, scenario-based planning methods are introduced to produce alternative visions for managing urban growth in sustainable ways by incorporating various socio-environmental issues. However, even though modelling urban growth and associated impacts based on these scenarios have emerged to strengthen and quantify the future of urban policies and related planning actions, this process has a number of glitches. Major issues include the uncertainties associated with the selection of suitable methods to generate scenarios, identify indicators to be used to assess scenarios, evaluate scenarios to prioritize for policy formulation, and assess the impacts of policy scenarios. This paper aims to address the challenge of developing suitable policy scenarios for sustainable urban growth. As for the methodological approach, the study undertakes a thorough review of the literature and current practices, and conducts a two-round Delphi survey—involving experts from public, private and academic sectors specialized in the fields of urban planning, environmental planning, social planning, transportation modelling, and economic development. The expert driven policy scenarios are validated in a local context by comparing findings against the policy options as proposed in the South East Queensland Regional Plan 2017 (Australia). The findings offer valuable guidelines for planners, modellers, and policy makers in adopting suitable methods, indicators, and policy priorities, and thus, easing the daunting task of generating sustainable policy solutions.

Abstract Many years passed since the adoption of the Paris Agreement, which invites countries to determine their own contributions to climate change mitigation efforts. The Agreement does not offer a standard to measure progress but relies on a process of periodic stocktakes to inform ambition-raising cycles. To contribute to this process, we compare 2021 greenhouse gas emission projections up to 2030 against equivalent projections prepared back in 2015. Both sets of projections were prepared using the same bottom-up modelling approach that accounts for adopted policies at the time. We find that 2021 projections for the G20 as a group are almost 15% lower (approximately 6 GtCO2eq) in 2030 than projected in 2015. Annual emissions grow 1% slower in the coming decade than projected in 2015. This slower growth mostly stems from the adoption of new policies and updated expectations on technology uptake and economic growth. However, around one-quarter of these changes are explained by the effects of the COVID-19 pandemic on short-term emissions and economic forecasts. These factors combined result in substantially lower emission projections for India, the European Union plus the UK (EU27 + UK), the Unites States, Russia, Saudi Arabia, and South Africa. We observe a remarkable change in South African projections that changed from a substantial increase to now a decline, driven in part by the planned phase-out of most of its coal-based power. Emissions in India are projected to grow slower than in 2015 and in Indonesia faster, but emissions per capita in both countries remain below 5 tCO2eq in 2030, while those in the EU27 + UK decline faster than expected in 2015 and probably cross the 5 tCO2eq threshold before 2030. Projected emissions per capita in Australia, Canada, Saudi Arabia, and the United States are now lower than projected in 2015 but remain above 15 tCO2eq in 2030. Although emission projections for the G20 improved since 2015, collectively they still slightly increase until 2030 and remain insufficient to meet the Paris Agreement temperature goals. The G20 must urgently and drastically improve adopted policies and actions to limit the end-of-century warming to 1.5 °C.

Indonesia is one of the largest importers of soybeans in the world because domestic production is not enough to meet domestic demand for soybeans. Domestic production conditions continue to fall, so the government imports soybeans from abroad to meet the national supply. These problems must be understood so that the driving factors that cause these problems are known. In this study, we developed a system dynamics model to visualize the soybean production system. The researchers’ simulation model represented the soybean production system in 2000-2022 and with projection until 2043. This study focus on soybean production system. In conditions without policy, soybean production in Indonesia will be 189.217 tons in 2043. Production of this size cannot meet the demand for soybeans, which reached 3,645,400 tons in 2043. Resolving these issues requires several steps to obtain the expected solution. The policy scenarios implemented were a) a scenario without policy, b) a scenario of increasing productivity, c) a scenario of increasing crop area policy, and d) a combination scenario of increasing productivity and harvest area simultaneously. Of the several scenarios tested in the simulation model, the combination scenario of increasing productivity by 3 tons/ha and increasing soybean harvest area by 35% per year is the policy scenario that will quickly increase production and reduce imports (optimistic scenario). Simulation output data shows that soybean production can reach 3,670,510 tons to meet the demand for soybeans in 2029.

This research aims to study and develop a model to demonstrate the causal relationships of factors used to forecast CO2 emissions from energy consumption in the industrial building sector and to make predictions for the next 10 years (2024–2033). This aligns with Thailand’s goals for sustainability development, as outlined in the green economy objectives. The research employs a quantitative research approach, utilizing Linear Structural Relationships based on a Latent Growth Model (LISREL-LGM model) which is a valuable tool for efficient country management towards predefined green economy objectives by 2033. The research findings reveal continuous significant growth in the past economic sector (1990–2023), leading to subsequent growth in the social sector. Simultaneously, this growth has had a continuous detrimental impact on the environment, primarily attributed to the economic growth in the industrial building sector. Consequently, the research indicates that maintaining current policies would result in CO2 emissions from energy consumption in the industrial building sector exceeding the carrying capacity. Specifically, the growth rate (2033/2024) would increase by 28.59%, resulting in a surpassing emission of 70.73 Mt CO2 Eq. (2024–2033), exceeding the designated carrying capacity of 60.5 Mt CO2 Eq. (2024–2033). Therefore, the research proposes strategies for country management to achieve sustainability, suggesting the implementation of new scenario policies in the industrial building sector. This course of action would lead to a reduction in CO2 emissions (2024–2033) from energy consumption in the industrial building sector to 58.27 Mt CO2 Eq., demonstrating a decreasing growth rate below the carrying capacity. This underscores the efficacy and appropriateness of the LISREL-LGM model employed in this research for guiding decision making towards green economy objectives in the future.

To achieve sustainable economic growth, Saudi Vision 2030’s target is to improve Saudi Arabia’s ranking on the Global Competitiveness Index from 25 in 2015–2016 to within the top 10 by 2030. Saudi Arabia also aims to increase the share of non-oil exports in the non-oil GDP from 16% in 2016 to 50% by 2030. For policymakers to make informed decisions to achieve these goals, they need to understand the driving forces of Saudi Arabia’s competitiveness. To this end, we consider the real effective exchange rate (REER) as a measure of external price competitiveness, as it captures domestic and global price changes. We then examine the REER using a two-stage modeling framework. First, we estimate the REER equation, which allows us to assess the impacts of the determinants and evaluate currency misalignments as a competitiveness indicator. Second, we extend the KAPSARC Global Energy Macroeconometric Model (KGEMM) with the estimated equation, which provides a framework for simulating the competitiveness impacts of the theoretically formulated determinants and other variables relevant to policymakers. The framework also allows us to account for feedback loops. We conduct a policy scenario analysis to quantify the competitiveness effects of the Public Investment Fund’s (PIF) new strategy for 2021–2025. We derive the following policy insights. Authorities may wish to implement initiatives boosting future productivity and, thus, competitiveness, such as PIF investments. Policymakers should be regularly informed about currency misalignment. Government consumption and public investment projects should consider substituting imports with locally produced goods and services. Local content development would also help to diversify the Saudi economy. Finally, attracting more foreign investment and other assets from the rest of the world may lead to technological development and improvement in the economic, financial, and social infrastructure and business environment, all enhancing competitiveness.

Projection and/or forecasting of future electricity production and consumption is needed to ensure its availability. The purpose of this study is to present empirical facts regarding global electricity production (supply) and consumption needs (demand) in the last 30 years (1993-2022). The second objective is to project and create policy scenarios to meet global electricity needs in the future, until 2052. Using a quantitative descriptive paradigm to explain empirical facts based on quantitative data. Data were collected from electricity production and consumption reports by Enerdata, and the European Commission during the period 1993-2022. Data analysis uses a Business-As-Usual (BAU) scenario model with the help of regression. It was found that global electricity consumption and production spread across Europe, CIS-Russia, North America, Latin America, Asia, the Pacific, Africa, and the Middle East during 1993-2022 are under each other, and show an increasing trend every year. Since 2003, Asia has had the largest electricity production and consumption. To meet global electricity needs until 2052, the policy scenario needed is the need for additional efforts from current production capacity. Asia is the largest, namely 370.05% or 12.33% per year.

The conflict between excessive population development and vulnerable resource (including water, food, and energy resources) capacity influenced by multiple uncertainties can increase the difficulty of decision making in a big city with large population scale. In this study, an adaptive population and water–food–energy (WFE) management framework (APRF) incorporating vulnerability assessment, uncertainty analysis, and systemic optimization methods is developed for optimizing the relationship between population development and WFE management (P-WFE) under combined policies. In the APRF, the vulnerability of WFE was calculated by an entropy-based driver–pressure–state–response (E-DPSR) model to reflect the exposure, sensitivity, and adaptability caused by population growth, economic development, and resource governance. Meanwhile, a scenario-based dynamic fuzzy model with Hurwicz criterion (SDFH) is proposed for not only optimizing the relationship of P-WFE with uncertain information expressed as possibility and probability distributions, but also reflecting the risk preference of policymakers with an elected manner. The developed APRF is applied to a real case study of Beijing city, which has characteristics of a large population scale and resource deficit. The results of WFE shortages and population adjustments were obtained to identify an optimized P-WEF plan under various policies, to support the adjustment of the current policy in Beijing city. Meanwhile, the results associated with resource vulnerability and benefit analysis were analyzed for improving the robustness of policy generation.