Scenario design for macrofinancial stress testing

This paper provides a novel empirical approach to scenario design for selecting a stress scenario for international macrofinancial variables. The scenario design framework is composed of several building blocks. First, multiple scenarios on the risk factors are generated by simulating a multi-country large Bayesian vector autoregression. Second, we take the perspective of a representative investor who aims to select a severe-yet-plausible scenario for a set of systematic risk factors following a factor-investing strategy. Moreover, we compare the stress scenarios selected under different approaches to measure plausibility (the Mahalanobis distance and entropy pooling under subjective views with a clear economic narrative). Finally, we compare our scenario design approach with a historical scenario approach in terms of its ability to select a stress scenario in the run-up to a rare adverse event such as the Covid-19 pandemic. We give evidence that our framework is suitable for the selection of a proper forward-looking severe-yet-plausible macrofinancial stress scenario.

Scenario Analysis on the Macroeconomic Impact of COVID-19: A Computable General Equilibrium Approach

This article uses the Computable General Equilibrium Model (CGE) of an open economy to analyze the impact of the COVID-19 pandemic on an open economy and industry sub-sectors, using the 2017 China Social Accounting Matrix (SAM) table data. The results have shown that, overall, the COVID-19 pandemic has harmed the economy extensively. The residential sector has been the most severely affected sector, particularly the hotels and catering services industries. Resident consumption demand is the most deeply affected part of all industries in all scenarios. Stabilizing employment and expanding demand is therefore an important mission for the government.

Potential pathways to reach energy-related CO2 emission peak in China: analysis of different scenarios.

The prevalence of global unilateralism and the shock of COVID-19 brought considerable uncertainty to China's economic development. Consequently, policy selection related to the economy, industry, and technology is expected to significantly impact China's national economic potential and carbon emission mitigation. This study used a bottom-up energy model to assess the future energy consumption and CO2 emission trend before 2035 under three scenarios: a high-investment scenario (HIS), a medium-growth scenario (MGS), and an innovation-driven scenario (IDS). These were also used to predict the energy consumption and CO2 emission trend for the final sectors and calculate each sector's mitigation contribution. The main findings were as follows. Firstly, under HIS, China would achieve its carbon peak in 2030, with 12.0 Gt CO2. Moderately lowering the economic growth rate to support the low-carbon transition of the economy by boosting the development of the low-carbon industry and speeding up the employment of key low-carbon technologies to improve energy efficiency and optimize energy structure in the final sectors, the MGS and the IDS would achieve carbon peak approximately in 2025, with a peak of 10.7 Gt CO2 for the MGS and 10.0 Gt CO2 for the IDS. Several policy recommendations were proposed to meet China's nationally determined contribution targets: instigating more active development goals for each sector to implement the "1+N" policy system, taking measures to accelerate the R&D, boosting the innovation and application of key low-carbon technologies, strengthening economic incentives, forming an endogenous driving force for market-oriented emission reduction, and assessing the climate impacts of new infrastructure projects.

COVID-19 and Sustainable Development Goals (SDGs): Scenario analysis through fuzzy cognitive map modeling.

The COVID-19 crisis has immensely impacted the implementation of the 2030 Agenda for Sustainable Development worldwide. This research aims at providing a policy response to support achieving the Sustainable Development Goals (SDGs) taking the COVID-19 long-term implications into account. To do so, a qualitative analytical method was employed in the following four steps. First, a fuzzy cognitive map was developed to specify causal-effect links of the interdependent SDGs in Iran as a developing country in the Middle East. Second, potential effects of the pandemic on the SDGs achievement were analyzed. Third, five strategies were formulated, including green management, sustainable food systems, energizing the labor market, inclusive education, and supporting research and technology initiatives in the energy sector. And finally, different scenarios corresponding to the five proposed strategies were tested based on the identified interconnections among the SDGs. The analysis showed that applying each of the five considered strategies or their combination would mitigate the effect of COVID-19 on the SDGs only in case of a medium pandemic activation level. Moreover, implementing a single strategy with a high activation level leads to better outcomes on the SDGs rather than applying a combination of strategies in low or medium activation levels during the pandemic situation. The provided insights support stakeholders and policy-makers involved in the post-COVID-19 recovery action plan towards implementing the 2030 Agenda for Sustainable Development.

Quantifying the spatiotemporal evolution characteristics of medical waste generation during the outbreak of public health emergencies.

Based on the medical waste quantity and patient data during the corona virus disease 2019 (COVID-19) outbreak in China, this study used scenario analysis to quantitatively analyze the temporal and spatial evolution of medical waste generation during the pandemics. First, the results show that the estimated medical waste per capita reached 15.4 kg/day if only patients were considered in Scenario 1, while the figures were reduced to 3.2 kg/day in Scenario 2 and 2.5 kg/day in Scenario 3 when the effects of both the patient type and the number of medical staffs were considered. The estimated results also demonstrated that the per capita medical waste related to the epidemic showed the characteristics of a U-shaped and trailing phenomenon over time. Then, the amount of medical waste related to the COVID-19 generated that generated due to COVID-19 was estimated in Hubei, Heilongjiang, Zhejiang, Henan and Hunan provinces under Scenario 2 and Scenario 3. The results indicated that the spatiotemporal evolution characteristics of five provinces show the significant differences, and the patient type has a remarkable influence on the generation of medical waste. Finally, a novel decomposition-ensemble approach was designed to make a better short-term forecasting effect for future medical waste generation in different provinces. Supplementary Information: The online version contains supplementary material available at 10.1007/s10163-022-01523-5.

Impact assessment of China's inter-provincial trade on trade-related sustainable development goals

Assessing progress towards achieving the Sustainable Development Goals (SDGs) is among the most pressing areas for sustainability research. Both international and inter–provincial trade has substantial impacts on sustainability. However, little is known about the impacts of inter–provincial trade on progress towards achieving the SDG targets and the relationships among SDG indicators through time and space. Here we, taking Chinese inter–provincial trade as a study case, used a spatiotemporal approach and the multi–regional input–output (MRIO) model to examine changes in six SDG indicators and their relationships within China in the year 2002, 2007, 2010, 2012, 2015, and 2017. The results showed that (1) Chinese inter–provincial trade improved the trade–related SDG target scores of 16 provinces out of the evaluated 30 provinces but reduced the trade–related SDG target scores of the remaining 14 provinces. (2) Chinese inter–provincial trade and distant trade were more beneficial for achieving the trade–related SDG targets in developed provinces (e.g., Beijing), which thus improved China's overall SDG target scores. In contrast, Chinese inter–provincial trade suppressed the trade–related SDG target scores of developing provinces (e.g., Guangxi). (3) Individual SDG indicators, SDG target bundles, and interactions among SDG indicators changed across both time and space. (4) The no–trade scenario in Hubei province during the COVID–19 pandemic will have a clearly inhibiting effect on China's overall SDG target scores. Besides, trade with adjacent provinces would improve Hubei's SDG target scores, while these trades have more negative effects (approximately 50–83% of provinces suffered from greater reductions in SDG target scores) on Hubei's adjacent provinces. Our study suggests the spatiotemporal dynamic characteristics of SDG indicators and their interactions deserve more attention, which can help identify the drivers behind these changing relationships.

Propelling steady growth and high-quality development through deeper reform and more comprehensive opening up: Outlook, policy simulations, and reform implementation-A summary of the Annual SUFE Macroeconomic Report (2021-2022)

China's economy underwent a steady recovery in 2021. Investment grew steadily with structural improvement. Exports and imports surged while trade surplus expanded. On the other hand, although labor market conditions improved, income distribution worsened, contributing to sluggish growth in consumption, whereas the gap between consumer price index and producer price index widened, and the profits of enterprises of different sizes diverged, which may go beyond how they are correlated with the locations of the enterprises in the chain of production and trade. While proper liquidity was maintained with prudent monetary policy, risk spillover rose in the financial system, particularly for small and medium-sized banks. Household and local government debts remained at relatively high levels, further dragging down growth in consumption and infrastructure investment. The "dual carbon" goals exerted downward pressure on near-term growth in trading off their long-term benefits. The economy also faced challenges in its external environment in the midst of the prolonged COVID-19 pandemic aboard, trade protectionism, and the readjustment of the global value chain. Moreover, excessive supervision and inadequate implementation disturbed China's economy, resulting in declined market vitality and confidence of market participants. Based on the Institute for Advanced Research-China Macroeconomic Model, the baseline real gross domestic product growth rate is projected to be 5.5% in 2022. Alternative scenario analyses and policy simulations are conducted, in addition to the benchmark forecast, to reflect the influences of various risks and possible favorable situations. The findings suggest that China should deepen reform and open up more comprehensively and initiatively, while special effort should be placed on providing accommodative policy and friendly public opinion environment, to facilitate steady growth and propel high-quality development. A comprehensive macroeconomic governance framework with Chinese characteristics must be developed from systems thinking, to resolve the various issues, internal and external, cyclical and secular, structural and institutional, in an all-inclusive and coherent manner.

Research on the potential for China to achieve carbon neutrality: A hybrid prediction model integrated with elman neural network and sparrow search algorithm.

China's carbon reduction is of substantial significance in combating global climate change. In the context of the COVID-19 epidemic hit and economic and social development uncertainty, this study intends to discover whether China can attain the strategic destination of carbon peaking by 2030 and carbon neutrality by 2060 on schedule. Toward this aim, the grey relation analysis (GRA) is applied to filter the elements influencing carbon emissions to downgrade the dimensionality of indicators. A hybrid prediction is proposed integrated with Elman neural network (ENN) and sparrow search algorithm (SSA) to explore the potential for China to carbon neutrality from 2020 to 2060. The results reveal eight elements including GDP per capita, population, urbanization, total energy consumption and others are highly correlated with carbon emissions. China has a good chance of carbon peaking from 2028 to 2030, with a value of 11568.6-12330.5 Mt, while only one scenario can achieve carbon neutrality in 2060. In the neutral scenario, China should reach a proportion of renewable energy exceeding 80%, the urbanization rate reaching 85% and energy consumption controlling within 6.5 billion tons. A set of countermeasures for carbon abatement are presented to facilitate the implementation of carbon neutrality strategy.

The management of COVID-19 epidemic: estimate of the actual infected population, impact of social distancing and directions for an efficient testing strategy. The case of Italy

This work focuses on the so called ‘first wave' of COVID-19 epidemic (21 February–10 April 2020) and aims at outlining a viable strategy to contain the COVID-19 spread and efficiently plan an exit from lockdown measures. It offers a model to estimate the total number of actual infected among the population at national and regional level inferring from the lethality rate, to fill the proven gap with the number of officially reported cases. The result is the reference population used to develop a forecasting exercise of new daily cases, compared to the reported ones. The eventual discrepancy is analysed in terms of compliance with the restrictive measures or to an insufficient number of tests performed. This simulation indicates that an efficient testing policy is the main actionable measure. Furthermore, the paper estimates the optimal number of tests to be performed at national and regional level, in order to be able to release an increasing number of individuals from restrictive measures. Copyright © 2022 Inderscience Enterprises Ltd.

Tourism Development Policy and the Use of Scenario Analysis: A synthesis model

COVID-19's pandemic made us learn to live with a renewed sense of limits and a new level of uncertainty. One of the governance responses that emerged from this panorama was the shift to scenario analysis, which generates narratives about multiple future possibilities. This paper attempts to answer the question of why and how to use scenario analysis when defining tourism development policy. In this study, a semi-systematic investigation is conducted to broaden the scope of discussion and explore new paths associated with the topic of tourism development policy. It is believed that the use of scenarios in tourism development policy can prove to be a valuable experimental technique for developing innovative ideas. With that end, this paper proposes a scenario development process model for policy and decision makers. As in any exploratory study, there are limitations, including the difficulty to generalising certain assumptions.

The management of COVID-19 epidemic: estimate of the actual infected population, impact of social distancing and directions for an efficient testing strategy. The case of Italy

This work focuses on the so called 'first wave' of COVID-19 epidemic (21 February-10 April 2020) and aims at outlining a viable strategy to contain the COVID-19 spread and efficiently plan an exit from lockdown measures. It offers a model to estimate the total number of actual infected among the population at national and regional level inferring from the lethality rate, to fill the proven gap with the number of officially reported cases. The result is the reference population used to develop a forecasting exercise of new daily cases, compared to the reported ones. The eventual discrepancy is analysed in terms of compliance with the restrictive measures or to an insufficient number of tests performed. This simulation indicates that an efficient testing policy is the main actionable measure. Furthermore, the paper estimates the optimal number of tests to be performed at national and regional level, in order to be able to release an increasing number of individuals from restrictive measures.

Exploring the driving forces and scenario analysis for China's provincial peaks of CO2 emissions

Understanding the drivers and peaks of CO2 emissions at the provincial level plays a crucial role in achieving the goals of China's CO2 emissions peak by 2030. This research combines the spatial-temporal Logarithmic Mean Division Index with scenario analysis to empirically explore the drivers and peaks of CO2 emissions in 30 provinces in China during 1997 and 2020, considering COVID-19 effects. The results show that the energy structure has replaced the energy intensity as the main factor of emission reduction in 2020. The CO2 emission driving mechanism at the provincial level is different from that at the country level. The population restrains the CO2 emissions in Heilongjiang, Sichuan, and Guizhou. The energy structure increases CO2 emissions in Hainan, Ningxia, Shanxi, and Xinjiang. The role of driving factors to CO2 emissions varies greatly among provinces. The population effect is strong in Shandong, Guangdong, and Henan. The economic effect is significant in Shanghai, Jiangsu, and Tianjin. The energy intensity effect is remarkable in Shanxi, Ningxia, and Inner Mongolia. The energy structure effect is profound in Shanxi, Inner Mongolia, and Henan. Based on our findings, China's CO2 emissions peak will occur in 2030 and 2025 under baseline and green development scenarios. Many provinces have already reached their peaks, including Chongqing, Yunnan, Beijing, Tianjin, Qinghai, Shanghai, Jilin, Hubei, Heilongjiang, Hebei, Sichuan, Anhui, Guizhou, and Henan. However, Xinjiang and Shanxi will not reach their peaks by 2030. Based on the findings, this paper put forward several policy implications.

A Systematic Two-Phase Study of COVID-19 Pandemic Impact on Sustainable Development Goals: A Case Study of Iran

To respond to the urgent call for preventive action against COVID-19 pandemic implications for societies, this research is carried out. The main aim of our research is providing a new insight for the effects of the newly emerged restrictions by COVID-19 on the SD Goals (SDGs). This research, for the first time applied a two-phase qualitative approach for supporting the SDGs achievement post-COVID in Iran, as a developing country in the Middle East. In the first phase, using a fuzzy Delphi method, the SDGs affected by COVID-19 were identified. In the next phase, a fuzzy cognitive map, as a qualitative system dynamics modeling, was conducted to specify the key interconnections among the SDGs post COVID-19. Finally, three strategies including focus on people in vulnerable situation, support for industrial units and small and medium-sized enterprises, and national aggregation to Fight COVID-19 were examined. As a result, different scenarios associated with the three proposed strategies were tested based on the identified interconnections among the SDGs to reduce the potential negative effects of COVID-19 crisis on the achievement of the SDGs. The results provide a decision support for stakeholders and policy makers involved in SD action plan. © Iran University of Science and Technology 2022.

Time for change? Scenario analysis on buyer seller negotiations

Purpose - This study aims to provide probable future developments in the form of holistic scenarios for business negotiations. In recent years, negotiation research did not put a lot of emphasis on external changes. Consequently, current challenges and trends are scarcely integrated, making it difficult to support negotiation practice perspectively. Design/methodology/approach - This paper applies the structured, multi-method approach of scenario analysis. To examine the future space of negotiations, this combines qualitative and quantitative measures to base our analysis on negotiation experts' assessments, estimations and visions of the negotiation future. Findings - The results comprise an overview of five negotiation scenarios in the year 2030 and of their individual drivers. The five revealed scenarios are: digital intelligence, business as usual, powerful network - the route to collaboration, powerful network - the route to predominance and system crash. Originality/value - The scenario analysis is a suitable approach that enables to relate various factors of the negotiation environment to negotiations themselves and allows an examination of future changes in buyer-seller negotiations and the creation of possible future scenarios. The identified scenarios provide an orientation for business decisions in the field of negotiation.

A system dynamics approach to COVID-19 pandemic control: a case study of Iran

Purpose>The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster.Design/methodology/approach>To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results.Findings>The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital’s capacity by adding appropriate equipment.Research limitations/implications>The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper.Originality/value>The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework.

Evaluating the Effectiveness of the COVID-19 Emergency Outbreak Prevention and Control Based on CIA-ISM.

The COVID-19 pandemic, characterized by high uncertainty and difficulty in prevention and control, has caused significant disasters in human society. In this situation, emergency management of pandemic prevention and control is essential to reduce the pandemic's devastation and rapidly restore economic and social stability. Few studies have focused on a scenario analysis of the entire emergency response process. To fill this research gap, this paper applies a cross impact analysis (CIA) and interpretive structural modeling (ISM) approach to analyze emergency scenarios and evaluate the effectiveness of emergency management during the COVID-19 crisis for outbreak prevention and control. First, the model extracts the critical events for COVID-19 epidemic prevention and control, including source, process, and resultant events. Subsequently, we generated different emergency management scenarios according to different impact levels and conducted scenario deduction and analysis. A CIA-ISM based scenario modeling approach is applied to COVID-19 emergency management in Nanjing city, China, and the results of the scenario projection are compared with actual situations to prove the validity of the approach. The results show that CIA-ISM based scenario modeling can realize critical event identification, scenario generation, and evolutionary scenario deduction in epidemic prevention and control. This method effectively handles the complexity and uncertainty of epidemic prevention and control and provides insights that can be utilized by emergency managers to achieve effective epidemic prevention and control.

National emissions inventory and future trends in greenhouse gases and other air pollutants from civil airports in China.

Civil aviation is an important source of air pollutants, but this field has received insufficient attention in China. Based on the standard emissions model of the International Civil Aviation Organization (ICAO) and actual flight information from 241 airports, this study estimated a comprehensive emissions inventory for 2010-2020 by considering the impacts of mixing layer height. The results showed that annual pollutant emissions rapidly trended upward along with population and economic growth; however, the emissions decreased owing to the impacts of the COVID-19 pandemic. In 2020, the emissions of carbon monoxide (CO), nitrogen oxides (NOX), particulate matter (PM), methane (CH4), nitrous oxide (N2O), carbon dioxide (CO2), and water vapor (H2O) were 34.34, 65.73, 0.10, 0.34, 0.40, 14,706.26, and 5733.11 Gg, respectively. The emissions of total volatile organic compounds (VOCs) from China's civil airports in 2020 were estimated at 17.20 Gg; the major components were formic acid (1.70 Gg), acetic acid (1.62 Gg), 1-butylene (1.03 Gg), acetone (0.96 Gg), and acetaldehyde (0.93 Gg). The distribution of pollutant emissions was consistent with the level of economic development, mainly in Beijing, Guangzhou, and Shanghai. In addition, we estimated future pollution trends for the aviation industry under four scenarios. Under the comprehensive scenario, which considered the impacts of economic growth, passenger turnover, cargo turnover, COVID-19, and technological efficiency, the levels of typical pollutants were expected to increase by nearly 1.51-fold from 2010 to 2035.

UNDERSTANDING ECONOMIC INSTABILITY DURING THE PANDEMIC: A FACTOR MODEL APPROACH

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model’s forecasting performance and extract some instability measures based on the factor model’s eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability. © 2022 by Emerald Publishing Limited.

The impact of optimal parcel locker locations on costs and the environment

Purpose>Last-mile delivery is associated with a negative environmental impact and high costs. The purpose of this paper is to develop an approach to designing stationary parcel locker (SPL) networks while minimizing both CO2 equivalent (CO2e) emissions and costs during delivery and pick-up.Design/methodology/approach>This study uses a multinomial logit model to evaluate recipients' willingness to use SPLs based on their availability at home and travel distance. To determine optimal SPL locations, this study formulates a mixed-integer linear programming model.Findings>The empirical study of different regional clusters reveals that optimal SPL locations can generate cost savings of up to 11.0%. SPLs have a positive impact on total CO2e emission savings in urban areas (i.e. up to 2.5%), but give rise to additional emissions (i.e. 4.6%) in less populated areas due to longer travel distances during the pick-up process.Originality/value>This paper optimizes SPL locations and the ecological effect of SPLs by minimizing emissions and costs simultaneously. Furthermore, it extends existing discrete choice models by also including recipients' availability at home, increasing the accuracy of recipients' preferences. So far, the effect of SPLs has been studied for metropolitan areas only. A global logistics service provider shared a real dataset which allows us to study seven different regional clusters ranging from rural areas to large cities. Thus, this study contributes to the field of sustainable urban logistics.

Will COVID-19 affect China's peak CO2 emissions in 2030? An analysis based on the systems dynamics model of green finance

Achieving the peak of carbon dioxide (CO2) emissions requires a large amount of green and low-carbon investment. Whether the green finance system can efficiently support the capital need for achieving the CO2 emissions target in the context of the COVID-19 epidemic is a matter of concern. This paper constructs a system dynamics model (SD model) to illustrate the quantitative relationship between the green finance system and CO2 emissions and introduce the COVID-19 epidemic as a variable to analyze ten simulation scenarios regarding the carbon emissions commitment realization under different green finance and economic growth status. It is shown that: (1) Regardless of the impact of COVID-19, China can meet its commitment by reaching its CO2 emissions peak in 2030 and realizing a 20% non-fossil energy proportion in 2025;(2) Under the impact of the epidemic, the goal can not be obtained in all energy consumption scenarios when the government expenditure on the environment is low. The target year of reaching CO2 emissions peak becomes 2033, 2037, and 2040. The results indicate that reducing government expenditure on environment protection makes the CO2 emissions peak target less likely to be achieved within a given time frame. We also concluded with important policy implications according to the result of the simulations. Overall, this study makes a reference for other economies and researchers to quantitatively predict the interaction relationship between the green finance system and CO2 emissions in the context of COVID-19, which provides policymakers with insights into a joint power of energy consumption upgrading and green capital guidance.