India's Economic Growth and Environmental Sustainability: What are the Tradeoffs?

Objective. To evaluate the socioeconomic impact of COVID-19 in Rostov region. Material and methods. This article focuses on social and economic aspects of COVID-19. By estimating direct medical costs and gross domestic product (GDP) losses (sick leave days), we assessed the economic damage caused by the pandemic in Rostov region. When calculating direct medical costs, we considered per-case costs of completed outpatient and inpatient treatment and costs of laboratory testing (identification of coronavirus). When calculating the costs associated with paid sick Rostov region in 2020. Results. We found that COVID-19-associated direct medical costs and GDP losses (sick leave days) were 3,174,344,917.04 RUB. Almost half (42.7%) of direct medical costs were spent on outpatient treatment. GDP losses were 1,014,047,274.24 RUB. Conclusion. In addition to the direct impact on the health of vulnerable individuals, current pandemic will inevitably cause longterm socioeconomic consequences for both people and the economy. The assessment of the socioeconomic impact of COVID-19 is important to develop effective preventive and anti-epidemic measures, as well as to make optimal management decisions. Key words: GDP losses, pandemic, direct medical costs, PCR diagnostics, Rostov region, COVID-19

BackgroundIn responding to Covid-19, governments have tried to balance protecting health while minimizing gross domestic product (GDP) losses. We compare health-related net benefit (HRNB) and GDP losses associated with government responses of the UK, Ireland, Germany, Spain and Sweden from UK healthcare payer perspective.MethodsWe compared observed cases, hospitalizations and deaths under ‘mitigation’ to modelled events under ‘no mitigation’ to 20 July 2020. We thus calculated healthcare costs, quality adjusted life years (QALYs), and HRNB at £20,000/QALY saved by each country. On per population (i.e. per capita) basis, we compared HRNB with forecast reductions in 2020 GDP growth (overall or compared with Sweden as minimal mitigation country) and qualitatively and quantitatively described government responses.ResultsThe UK saved 3.17 (0.32–3.65) million QALYs, £33 (8–38) billion healthcare costs and £1416 (220–1637) HRNB per capita at £20,000/QALY. Per capita, this is comparable to £1455 GDP loss using Sweden as comparator and offsets 46.1 (7.1–53.2)% of total £3075 GDP loss. Germany, Spain, and Sweden had greater HRNB per capita. These also offset a greater percentage of total GDP losses per capita. Ireland fared worst on both measures. Countries with more mask wearing, testing, and population susceptibility had better outcomes. Highest stringency responses did not appear to have best outcomes.ConclusionsOur exploratory analysis indicates the benefit of government Covid-19 responses may outweigh their economic costs. The extent that HRNB offset economic losses appears to relate to population characteristics, testing levels, and mask wearing, rather than response stringency.

Energy, economic and environmental impact of government fines in China's carbon trading scheme

The health and well-being of human populations – and the very existence of low-lying island states – depend on an ambitious outcome at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change, (Paris, 30 November to 11 December 2015). Unless the world dramatically reduces emissions of greenhouse gases, especially carbon dioxide from burning of fossil fuels, it is estimated that global average temperature will increase by 2.6–4.8 °C and sea level will rise by up to a metre around the end of this century.1 These changes would have major health impacts. By the 2030s – now less than 15 years away – approximately 250 000 additional deaths are projected to occur every year from malnutrition, malaria, diarrhoea and heat stress attributable to climate change.2 Climate change will disproportionally affect small island states, because of their relatively small land area, high population density and dependence on local ecosystems for subsistence. Rising sea levels threaten to make low-lying island nations such as Kiribati, the Marshall Islands and Tuvalu uninhabitable. Understandably, Pacific island leaders have called for increases in global average temperatures to be limited to less than 1.5 °C.3 Pacific island countries are confronted with a triple burden of noncommunicable diseases, infectious diseases and climate change impacts. In some Pacific island states, mortality rates from noncommunicable diseases are already among the highest in the world. Since 2012, there have been over 40 large infectious disease outbreaks in the region: most were caused by climate-sensitive diseases such as dengue, chikungunya and Zika virus infections.4 There have also been several record-breaking extreme climate events in the Pacific recently. Extreme events often damage or destroy health facilities, disrupting essential health services when they are needed most urgently. For example, in Vanuatu, cyclone Pam seriously damaged many health facilities: two hospitals, 19 health centres and 50 dispensaries in 22 affected islands. In the most affected province, 21 of 24 health facilities were damaged. The economic impact was estimated to be more than 60% loss of gross domestic product.5 The World Health Organization (WHO) has been working with ministries of health and other stakeholders of Pacific island countries to build climate resilient health systems. Although a disease-specific approach is useful initially, in the long run a comprehensive intersectoral programme is required. As recommended by WHO,6 this programme should address (i) health governance and policies tackling climate risks; (ii) health information, integrated surveillance and climate early warning systems; and (iii) preventive and curative services (water and sanitation, pest and vector control, food safety, disaster risk management for health, safe and environmentally sustainable health facilities, vaccination and child health services).7 In the past five years, WHO has assisted thirteen Pacific island countries to develop national climate change and health vulnerability and impact assessments to guide health system adaptation plans. These assessments highlighted common climate risks such as direct impacts of extreme weather events (e.g. drowning and injuries), waterborne and vector-borne diseases.8 In Fiji, where a recent outbreak of dengue fever affected over 20 000 people, dengue, leptospirosis, typhoid fever and diarrhoea were targeted as priority climate-sensitive diseases.9 Fiji has produced a strategic action plan for climate change and health for 2016–2020, including the development of early warning and response in vulnerable communities. However, as for other small island developing states, implementation will be a huge challenge. The health sector has received less than 1.5% of the multilateral funds for climate change adaptation distributed to date.10 The international response to climate change is a major opportunity to improve global health. For the first time, international commitments for reductions in carbon dioxide emissions, if fully implemented, would have a meaningful impact on global temperature rise.11 The Paris climate pact, if sufficiently ambitious and effective, would be considered not only a major turning point in climate change policy, but also a far-reaching public health treaty to protect and promote the health and well-being of vulnerable populations, particularly those of small island states.12

This paper is based on an attempt to describe the economic effects of a health program and to measure these effects. Multiple and complex factors make it difficult to determine the economic value of health investments. This problem of measurement may be solved by considering that any improvement of health means an increase and any deterioration of health indicates a decrease in the working capacities of the labor force. This paper suggests that death and disease cause losses of working time and gross domestic product (GDP). The example presented relates to malaria and to the effects of the malaria eradication program in Thailand. For the computation of economic losses due to malaria mortality and morbidity, a model was constructed in which the main variables are combined. The computations show the GDP lost year by year by the agricultural laborers who die or are sick as a result of malaria. The losses of GDP are calculated over a period of 12 years. Their downward trend is attributed to the impact (benefits) of the malaria eradication program. A separate series of computations (Table 2) indicates the economic losses of GDP that would be expected in the absence of a malaria eradication program.

The objective of this study was to estimate the economic consequences of neurosurgical disease in low- and middle-income countries (LMICs). The authors estimated gross domestic product (GDP) losses and the broader welfare losses attributable to 5 neurosurgical disease categories in LMICs using two distinct economic models. The value of lost output (VLO) model projects annual GDP losses due to neurosurgical disease during 2015-2030, and is based on the WHO's "Projecting the Economic Cost of Ill-health" tool. The value of lost economic welfare (VLW) model estimates total welfare losses, which is based on the value of a statistical life and includes nonmarket losses such as the inherent value placed on good health, resulting from neurosurgical disease in 2015 alone. The VLO model estimates the selected neurosurgical diseases will result in $4.4 trillion (2013 US dollars, purchasing power parity) in GDP losses during 2015-2030 in the 90 included LMICs. Economic losses are projected to disproportionately affect low- and lower-middle-income countries, risking up to a 0.6% and 0.54% loss of GDP, respectively, in 2030. The VLW model evaluated 127 LMICs, and estimates that these countries experienced $3 trillion (2013 US dollars, purchasing power parity) in economic welfare losses in 2015. Regardless of the model used, the majority of the losses can be attributed to stroke and traumatic brain injury. The economic impact of neurosurgical diseases in LMICs is significant. The magnitude of economic losses due to neurosurgical diseases in LMICs provides further motivation beyond already compelling humanitarian reasons for action.

Could the EU carbon border adjustment mechanism promote climate mitigation? An economy-wide analysis

Can national carbon emission trading policy effectively recover GDP losses? A new linear programming-based three-step estimation approach

Possible pathways for dealing with Japan's post-Fukushima challenge and achieving CO2 emission reduction targets in 2030

In this paper, provincial panel data for China during 1995–2015 and the time substitution data envelopment analysis (DEA) model were used to measure the influences of China’s carbon emissions reduction policy on economic growth under various reduction targets and to determine optimal economic growth and optimal carbon emissions of each province. In addition, this paper empirically examines the factors that influence the optimal economic growth and carbon emissions. The results indicate that not all provinces will suffer from a loss in gross domestic product (GDP) when confronted by the constraints of carbon emissions reductions. Certain provinces can achieve a win-win situation between economic growth and carbon emissions reductions if they are allowed to reallocate production decisions over time. Provinces with higher environmental efficiency, higher per capita GDP, smaller populations, and lower energy intensity might suffer from a larger loss in GDP. Therefore, they should set lower carbon emissions reduction targets.

The world, addressing to achieve rapid and drastic economic growth by relying on fossil fuel energy consumption, could increase already increasing level of carbon dioxide (CO2). Therefore, there is a growing consensus that environmental sustainability by using renewable energy is the only option to avoid environmental calamity. Therefore, according to the authors' best knowledge, this is the first work to look into the short and long-run nexus between economic growth, trade openness, renewable and fossil fuel energy consumption, along with gross capital formation, population growth, and life expectancy as additional variables in top 10 highest renewable energy-using (TRU) economies and top 10 highest fossil fuel-using (TFU) economies from 1991 to 2020, by employing advanced panel data econometric approach. After demonstrating cross-sectional dependency in panel data, the Westerlund cointegration test verifies the long-term link between the variables. A cross-sectional autoregressive distributed lag (CS-ARDL) econometric technique is used to show short- and long-run coefficient values. CS-ARDL estimates confirm that the economic growth, fossil fuel energy, trade openness, and gross capital formation increase carbon dioxide (CO2) emissions levels in the short run for TRU and FEU economies, except for gross capital formation for FEU economies. However, economic growth adds to CO2 emissions for only TRU economies, while fossil fuel energy consumption enhances CO2 emissions for both groups of economies in the long run. On the contrary, renewable energy reduces CO2 emissions in the short and long run, while human capital in only the short run. The inferences of this study present new intuitions and urge governments and policymakers to develop a reliable mechanism for investing capital to diversify the energy portfolio through the energy transition process to attain sustainable economic growth and promote awareness campaigns to draw the attention of human capital to environmentally friendly, clean, and green energy sources. Overall, the results recommended energy efficiency usage and ecological friendly innovative technologies to enhance and protect environmental quality.

Urban residential energy switching in China between 1980 and 2014 prevents 2.2 million premature deaths

This chapter examines the relationship between greenhouse gas (GHG) emissions and economic stability in D8 countries – Bangladesh, Egypt, Indonesia, Iran, Malaysia, Nigeria, Pakistan, and Turkey. It aims to assess whether emissions, particularly methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2), serve as economic indicators and how transitioning to renewable energy influences economic growth and environmental sustainability. The chapter employs statistical analysis using regression models to evaluate the impact of GHG emissions on key economic indicators such as gross domestic product (GDP), gross national income (GNI), inflation, and energy consumption. Data from the World Development Indicators (WDI) spanning 2001–2023 are utilized. The Environmental Kuznets Curve (EKC) and Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) models are applied to examine the interplay between emissions and economic growth. Additionally, sectoral analysis is conducted to highlight the role of renewable energy and policy measures in mitigating emissions. The results indicate a complex relationship between emissions and economic stability. Methane and nitrous oxide emissions exhibit a significant correlation with economic performance, whereas CO2 emissions show mixed effects. Countries investing in renewable energy, such as Indonesia and Malaysia, demonstrate improved economic resilience while reducing emissions. The chapter highlights the need for energy efficiency measures, investment in clean energy, and regional cooperation to balance economic growth with environmental sustainability. This chapter provides a comprehensive analysis of the economic impact of GHG emissions in D8 nations, offering policy recommendations to achieve sustainable development while maintaining economic stability.

Daily CO2 Emission Reduction Indicates the Control of Activities to Contain COVID-19 in China

Objective. To evaluate clinical efficacy and efficiency of new oral anticoagulants (NOACs) and sodium-glucose cotransporter 2 (SGLT-2) inhibitors in patients with coronary artery disease and concomitant cardiovascular diseases in national healthcare system. Material and methods. The mathematical model assessed clinical efficacy and efficiency of 2 groups of drugs: 1) NOACs (apixaban, dabigatran and rivaroxaban) compared to warfarin in patients with coronary artery disease and atrial fibrillation (AF); 2) SGLT-2 inhibitors (dapagliflozin and empagliflozin) in addition to standard treatment compared to standard treatment without SGLT-2 inhibitors in patients with coronary artery disease and chronic heart failure (CHF) with ejection fraction (EF) <40%. Prediction of all-cause mortality was based on clinical trials after previous systematic searching of appropriate data. We estimated direct medical costs of NOACs and SGLT-2 inhibitors, as well as indirect costs following losses in gross domestic product, cost of prevented death and economic effect per 1 ruble invested. Results. Fewer deaths and higher costs are expected in patients with coronary artery disease and AF receiving NOACs compared to warfarin. The cost of 1 prevented death ranged from 18.75 to 14.63 million rubles. Economic effect per 1 additionally invested ruble for 1, 3 and 7 years was 0.06, 0.124 and 0.267 rubles, respectively. Fewer deaths and higher costs are also assumed in patients with coronary artery disease and CHF (EF <40%) receiving SGLT-2 inhibitors+standard treatment compared to standard treatment alone. The cost of 1 prevented death ranged from 7.93 to 3.81 million rubles. Economic effect per 1 additionally invested ruble for 1, 3 and 7 years was 0.132, 0.271 and 0.583 rubles, respectively. Conclusion. New anticoagulants in patients with coronary artery disease and atrial fibrillation, as well as SGLT-2 inhibitors in patients with chronic heart failure and EF <40% can reduce mortality. Necessary additional costs will be partially offset by lower indirect costs associated with losses in gross domestic product. The cost of prevented death is lower in cohort of patients with coronary artery disease and chronic heart failure receiving SGLT-2 inhibitors compared to patients with coronary artery disease and atrial fibrillation receiving new anticoagulants. Economic effect per 1 ruble invested for SGLT-2 inhibitors exceeds the effect for new anticoagulants.