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This case study is part of the research project "Creating Indigenous Women's Green Jobs under Low-Carbon COVID-19 Response and Recovery in the Bolivian Quinoa Sector). I estimate the carbon footprint associated with the quinoa production in southern Bolivia based on primary information of 19 plots. Using a cradle-to-gate life cycle analysis approach, under the ISO 14067 standard, and analyzing various emission sources through the Cool Farm Tool, I determine that the carbon footprint generates an average of 741 .7 kg CO2e per plot; and an average of 267.4 kg CO2e per hectare. The main emission sources identified are the use of organic fertilizer (54%), the consumption of fossil fuels (35%) and the use of protection inputs (8%). Considering the declared unit of 1 kg of harvested quinoa, I obtain the carbon footprint results per product, with values ranging between 0.3 and 2.3 kg CO2e/kg of quinoa and an average of 0.98 kg CO2e/ kg of quinoa.

While economic growth has been the main goal of countries around the world, environmental problems such as air pollution have also arisen. Since the increase in economic uncertainty is limiting production capacity and consumers’ marginal propensity to consume, which reduces CO2 emissions, economic policy uncertainty has become one of the most important factors affecting CO2 emissions. COVID-19 has demonstrated that economic policy uncertainty reduces the enthusiasm of market participants, which, in turn, reduces energy demand and CO2 emissions. In order to further study the impact of economic policy uncertainty on air pollution, this study uses a panel model to empirically test the data for a sample of 15 countries covering the period from 1997 to 2019. According to the empirical results, we find that the economic policy uncertainty has a significant negative impact on per capita CO2 emissions. That is, the higher the uncertainty of economic policy, the lower the per capita CO2 emissions of countries. What’s more, this negative effect is larger in emerging market countries than in advanced countries.

AbstractChina’s long-term sustainability faces socioeconomic and environmental uncertainties. We identify five key systemic risk drivers, called disruptors, which could push China into a polycrisis: pandemic disease, ageing and shrinking population, deglobalization, climate change, and biodiversity loss. Using an integrated simulation model, we quantify the effects of these disruptors on the country’s long-term sustainability framed by 17 Sustainable Development Goals (SDGs). Here we show that ageing and shrinking population, and climate change would be the two most influential disruptors on China’s long-term sustainability. The compound effects of all disruptors could result in up to 2.1 and 7.0 points decline in the China’s SDG score by 2030 and 2050, compared to the baseline with no disruptors and no additional sustainability policies. However, an integrated policy portfolio involving investment in education, healthcare, energy transition, water-use efficiency, ecological conservation and restoration could promote resilience against the compound effects and significantly improve China’s long-term sustainability.

AbstractThis assessment by the Environmental Effects Assessment Panel (EEAP) of the United Nations Environment Programme (UNEP) provides the latest scientific update since our most recent comprehensive assessment (Photochemical and Photobiological Sciences, 2019, 18, 595–828). The interactive effects between the stratospheric ozone layer, solar ultraviolet (UV) radiation, and climate change are presented within the framework of the Montreal Protocol and the United Nations Sustainable Development Goals. We address how these global environmental changes affect the atmosphere and air quality; human health; terrestrial and aquatic ecosystems; biogeochemical cycles; and materials used in outdoor construction, solar energy technologies, and fabrics. In many cases, there is a growing influence from changes in seasonality and extreme events due to climate change. Additionally, we assess the transmission and environmental effects of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for the COVID-19 pandemic, in the context of linkages with solar UV radiation and the Montreal Protocol.

This study seeks to granularly document the fundamental aspects of the pandemic and its effect on the global scale and environmental steadiness. Structural equation modeling (SEM) is therefore utilized to analyze these constructs. The study results show that the pathway coefficient for the parameters, such as health awareness, naturalism, mediation, personal development, sustainability, sociability, empathy, and cooperation, illustrates that the parameters directly impact pandemic control and management. At the same time, the lockdown and social distance rules attain dire consequences on the “conventional” retail property sector. It might speed up the evolution procedure of different conduits retail plus the channel coupling performance of physical stores and, hence, cause changes in urban areas–retail sector. The pandemic isn’t necessarily leading to the shutdown of retail stores. Nonetheless, it might have a meaningful effect on the retail estate enterprise business. The results show a requirement for the rapid physical shop repositioning performance of different channels firms. The study presents a meaningful understanding and demonstrates many consequences for the retailers, Landlords, and equally policy crafting components tackling urban regeneration plus local economic advancement within the post-covid phase.

OBJECTIVE: There is a growing correlation between the rise in infectious diseases and climate change; however, little is known about the interactions and mixed effects of climate factors on infectious diseases. METHOD: We conducted a retrospective longitudinal study spanning 108 consecutive months from 2014 to 2022 in Can Tho, Vietnam to identify common infectious diseases (excluding tuberculosis, HIV, and COVID-19) and their associations with climate change and determine which common diseases presented concurrently with the COVID-19 period using multivariate linear regression, receiver operating characteristic (ROC) curve analysis, Bayesian kernel machine regression (BKMR) and orthogonal partial least squares discriminant analysis. RESULT: The five infectious diseases with the highest average incidence rates per 100,000 people were diarrhea; hand, foot, and mouth disease (HFMD); dengue fever; viral hepatitis; and influenza. Positive associations with humidity were observed for dengue fever and HFMD. Temperature was positively associated with malaria. Negative associations were found between humidity and both chickenpox and tetanus. Diarrhea (AUC = 0.79; 95 % CL = 0.70–0.87) and dengue fever (AUC = 0.74; 95 % CL = 0.62–0.83) emerged as the most influential diseases both before and during the COVID-19 period. In our BKMR analysis, we found a significant association between the combined influence of temperature and humidity and the occurrence of dengue fever and HFMD, especially when all climate factors were at or above their 60th percentile relative to their values at the 50th percentile. Temperature emerged as the primary driver associated with the occurrence of infectious diseases. CONCLUSION: These findings underscore the importance of implementing robust surveillance, prevention, and control measures by public health authorities in Can Tho. Initiatives like vaccination campaigns, vector control programs, public education on hygiene practices, and strengthening healthcare infrastructure are crucial for mitigating the spread of infectious diseases and safeguarding public health in the region.