NATURAL DISASTERS AND INDIVIDUAL ECONOMIC PERFORMANCE: A CASE STUDY FROM THE SLAVE LAKE WILDFIRE

Reducing disease can maintain personal individual income and improve societal economic productivity. However, estimates of income loss for multiple diseases simultaneously with thorough adjustment for confounding are lacking, to our knowledge. We estimate individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease). We used linked health tax data for New Zealand as a high-income country case study, from 2006 to 2007 to 2015 to 2016 for 25- to 64-year-olds (22.5 million person-years). Fixed effects regression was used to estimate within-individual income loss by disease, and cause-deletion methods to estimate economic productivity loss at the population level. Income loss in the year of diagnosis was highest for dementia for both men (US$8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Mental illness also had high income losses in the year of diagnosis (average of about $5,300 per year for males and $4,100 per year for females, for 4 subcategories of: depression and anxiety; alcohol related; schizophrenia; and other). Similar patterns were evident for prevalent years of diagnosis. For the last year of life, cancers tended to have the highest income losses, (e.g., colorectal cancer males: $17,786, 95% CI $15,555 to $20,018; females: $14,192, $12,357 to $16,026). The combined annual income loss from all diseases among 25- to 64-year-olds was US$2.72 billion or 4.3% of total income. Diseases contributing more than 4% of total disease-related income loss were mental illness (30.0%), cardiovascular disease (15.6%), musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%). The limitations of this study include residual biases that may overestimate the effect of disease on income loss, such as unmeasured time-varying confounding (e.g., divorce leading to both depression and income loss) and reverse causation (e.g., income loss leading to depression). Conversely, there may also be offsetting underestimation biases, such as income loss in the prodromal phase before diagnosis that is misclassified to "healthy" person time. In this longitudinal study, we found that income loss varies considerably by disease. Nevertheless, mental illness, cardiovascular, and musculoskeletal diseases stand out as likely major causes of economic productivity loss, suggesting that they should be prioritised in prevention programmes.

BackgroundReducing disease can maintain personal individual income and improve societal economic productivity. However, estimates of income loss for multiple diseases simultaneously with thorough adjustment for confounding are lacking, to our knowledge. We estimate individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease).Methods and findingsWe used linked health tax data for New Zealand as a high-income country case study, from 2006 to 2007 to 2015 to 2016 for 25- to 64-year-olds (22.5 million person-years). Fixed effects regression was used to estimate within-individual income loss by disease, and cause-deletion methods to estimate economic productivity loss at the population level.Income loss in the year of diagnosis was highest for dementia for both men (US$8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Mental illness also had high income losses in the year of diagnosis (average of about $5,300 per year for males and $4,100 per year for females, for 4 subcategories of: depression and anxiety; alcohol related; schizophrenia; and other). Similar patterns were evident for prevalent years of diagnosis. For the last year of life, cancers tended to have the highest income losses, (e.g., colorectal cancer males: $17,786, 95% CI $15,555 to $20,018; females: $14,192, $12,357 to $16,026).The combined annual income loss from all diseases among 25- to 64-year-olds was US$2.72 billion or 4.3% of total income. Diseases contributing more than 4% of total disease-related income loss were mental illness (30.0%), cardiovascular disease (15.6%), musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%).The limitations of this study include residual biases that may overestimate the effect of disease on income loss, such as unmeasured time-varying confounding (e.g., divorce leading to both depression and income loss) and reverse causation (e.g., income loss leading to depression). Conversely, there may also be offsetting underestimation biases, such as income loss in the prodromal phase before diagnosis that is misclassified to “healthy” person time.ConclusionsIn this longitudinal study, we found that income loss varies considerably by disease. Nevertheless, mental illness, cardiovascular, and musculoskeletal diseases stand out as likely major causes of economic productivity loss, suggesting that they should be prioritised in prevention programmes.

Background and objective Due to the recent demographic crisis driving the extinction of certain local communities, it is necessary to promote core projects to revitalize mountain village and rural areas. Methods This study collected a total of 443 policy projects based on major policies and major business plans in the forestry and agricultural sectors of the eight regional governments in 2021. The collected 443 policy projects related to revitalizing the mountain villages were classified by 3 experts into 5 domains in the forestry sector, which further broke down into 27 types, 3 domains in the agricultural sector, and which further broke down into 23 types, for a total of 8 domains and 50 types, and an online survey of 42 policy stakeholders was then conducted. Analysis methods were t-test and Importance-Performance Analysis (IPA), and implications were derived through comparison between forestry and agricultural sectors. Results The analysis produced several findings. first, it was positive that many projects in the sustainability field appeared in both forestry and agricultural sectors, but it was found that the policy projects needed to be reviewed due to the lack of types corresponding to projects in the intensive promotion area. Intensive core efforts included “discovering forest cultural assets” and “creating forests to improve the environment.” Second, it is necessary to pay attention to the forestry policy sector, as the forestry policy sector were generally lower in importance and performance than the agricultural policy sector. Third, the domains with high importance and performance to mountain village revitalization were “forest welfare services”, “improving settlement environment”, and “increasing incomes” while the domains with low importance and performance were mountain villages and forest culture areas. Conclusion In summary, to revitalize mountain villages in response to the issue of the pending extinction of local communities, forest welfare service projects should continuously be promoted and strategic responses are required for “discovering forest cultural assets” and “creating forests for environmental improvement,” which are suggested as key strategic project types.

Environmental policy is a set of objectives put in place for the protection of natural resources including water, air, soil, food, and other renewable resources. In addition, it has a considerable impact on the labor market and the income of employees in the environmental and forestry sectors. Environmental policy both directly and indirectly creates new jobs, the so-called green jobs. These jobs are designed to be long-term and sustainable, working towards both environmental and socio-economic stability. The aim of the research was to determine if there was a difference in income between the forestry and the environmental policy sectors. The primary objective of this paper was to propose ideas and instruments for strengthening the income of employees in both sectors to the creators of the new State Environmental Policy. This objective was met through appropriate research methods, including the field survey technique. In terms of statistics, we used descriptive characteristics and tested the hypothesis using a T-test. The data from 70 respondents were collected from January 2019 to December 2019. Half of the respondents were from the forestry sector, and the other half worked in environmental protection. Their total income was compared by using two selective T-tests, and the results showed a strong discrepancy. The analysis indicated that the average incomes in forestry are significantly lower than incomes in the environmental protection sector (in the Czech Republic). The statistically higher income of environmental workers reflects that the State Environmental Policy is effective, which improves the position of employees in the labor market. The forestry sector deserves similar concentrated state assistance, and therefore we recommend that the new State Environmental Policy in the Czech Republic also addresses the problem of low income in forestry.

The welfare and poverty of farmers are still being discussed by researchers regarding economic growth, the role of the agricultural, fisheries, and forestry sectors, as well as other factors. This research aims to analyze the achievements of economic growth, the role of the agricultural, fisheries, and forestry sectors in the economy, as well as the influence of income inequality, labor absorption, and infrastructure factors on welfare. This research was conducted in Southeast Sulawesi Province, Indonesia. Using a quantitative approach to test the relationship between variables based on secondary data during the 2013-2022 period. Data were analyzed using panel data regression. The results of the analysis show that the economic growth of Southeast Sulawesi Province in the last ten years has been less than encouraging, the same thing also happened in important sectors contributing to GRDP, namely the Agriculture, Forestry, and Fisheries Sectors. Furthermore, economic growth, economic inequality, employment, and infrastructure are proven to be able to have a direct influence in creating social welfare, but on the other hand, the Agriculture, Forestry, and Fisheries sectors have a negative influence. The factor that plays the most role in determining social welfare is income inequality. Then successively followed by infrastructure, employment, and finally economic growth.

Cardiovascular Outcomes in the Wake of Financial Uncertainty.

This study examines the effect of land area and income sources of agricultural households on their income in the context of reducing poverty in the agricultural sector. This study uses a quantitative approach and multiple regression methods as an analytical tool with data from the Agricultural Business Household Income Survey which is part of the 2013 Agricultural Census of West Sumatra Province. The data used in this study is cross-sectional data with total agricultural household income as the dependent variable and the amount of land owned by agricultural households, the area of ​​land controlled and cultivated by agricultural households, the percentage of agricultural household income from agricultural sector businesses, the percentage of agricultural household income from non-agricultural businesses, the percentage of other agricultural household income , the percentage of agricultural household income from wages in the agricultural sector, and the percentage of agricultural household income non-agricultural wage income. The results showed that the variable area of ​​land owned and the area of ​​land controlled and cultivated by agricultural households had a positive effect on their total income. Other variables that have a positive effect on total agricultural household income are the percentage of income from non-agricultural businesses and the percentage of non-agricultural wage income. On the other hand, the percentage of agricultural household income from agricultural sector businesses and the percentage of agricultural household income from wages in the agricultural sector have a negative effect on their total income. The results of this study suggest the need for redistribution of land to land-poor agricultural households and encourage them to diversify their business outside the agricultural sector but still based on agricultural products.


 Las emisiones antrópicas de dióxido de carbono (CO2), de metano (CH4) y de óxido nitroso (N2O), distinguen a estos gases de efecto invernadero (GEI) como los principales causantes del calentamiento global. Las cuales, provienen a escala mundial de los sectores energético (25.9%), industrial (19.4%), silvícola (17.4%) y agrícola (13.5%). Del total de los flujos de los GEI, el sector agrícola contribuye con 25% de CO2, 55-60% de CH4 y 65-80% de N2O. El CO2 se genera, principalmente, por la deforestación en las regiones tropicales, el CH4, por la ganadería y cultivos de arroz, el N2O por el uso de fertilizantes. México se ubica dentro de los 15 países con mayor producción de GEI. Aproximadamente, 30% del total de sus emisiones corresponden a los sectores agrícola, pecuario y forestal: dos terceras partes, se producen por las actividades de uso del suelo (incluido el cambio en su uso) y silvicultura el resto, por la agricultura y ganadería convencional. Debido a que el carbono orgánico del suelo se relaciona con la sustentabilidad de los sistemas agrícolas, y en su contenido incide el manejo del suelo, se han desarrollado diversas prácticas que permitan favorecer su almacenamiento, en los sectores agropecuario y forestal del país. Sin embargo, se requiere de la instrumentación de políticas públicas, que beneficien la adopción y promoción de dichas prácticas y que, a la vez, faciliten el cumplimiento de los compromisos que ha adquirido México a nivel nacional e internacional, para minimizar sus emisiones GEI.

COVID-19 creates uncertainty on business. SMEs in Indonesia need to take a loan to survive. Sharia banks could give credit financing based on Islamic law. This study has a purpose to observe if the total value of credit financing issued by sharia bank in Indonesia affects the total financing of agriculture, forestry, and aquaculture sectors. This study uses secondary data taken from ojk.co.id as the sample. The independent variable is credit financing use for capital venture and investment. Dependent variables are the total financing in the agricultural, forestry, and aquaculture sectors. Data is analyzed using the linear regression method. The data of each variable was also tested using a T-test to analyze if there were significant changes during the COVID-19 pandemic. Result showed that financing of capital venture has a moderate impact on the total of credit financing for agriculture and forestry sector, while investment is none. Moreover, the COVID-19 pandemic has no effect in changing the number of credits issued by sharia banks nor credit received by agriculture, forestry, and aquaculture sectors. Sharia bank implemented two pillars of maqashid, which are the protection of life and wealth in issuing credit financing. The total of credit issued by sharia banks in Indonesia affected Agriculture & forestry sectors for capital, but not for investment. Sharia bank implemented two pillars of maqashid, which are the protection of life and wealth in issuing credit financing

Objective To document the impact of stroke on employment income among people employed at the time of stroke. Design Population-based cohort study. Participants People hospitalized for stroke in Ontario, Canada (2010–2014) and people without stroke matched on demographic characteristics. Main measures Robust Poisson regression to estimate the effects of stroke on the probability of reporting employment income on tax returns over 3 years. Quantile regression difference-in-differences to estimate the changes in annual employment income attributable to stroke. Results Stroke survivors were increasingly less likely to report any employment income poststroke, incidence rate ratios (IRR) 0.87 at 1 year (95% confidence intervals [CI]; 0.85–0.88), 0.82 at 2 years (95% CI; 0.81–0.84) and 0.81 at 3 years (95% CI; 0.79–0.82). IRR for reporting at least 50% of prestroke income levels were 0.76 at 1 year (95% CI; 0.75–0.78), 0.75 at 2 years (95% CI; 0.73–0.77) and 0.73 at 3 years (95% CI; 0.71–0.75). IRR for reporting at least 90% of prestroke income levels were 0.72 at 1 year (95% CI; 0.70–0.74), 0.66 at 2 years (95% CI; 0.64–0.68) and again 0.66 at 3 years (95% CI; 0.64–0.68). Relative changes in annual employment income attributable to stroke varied from a decrease of 13.8% (95% CI; 8.7–18.9) at the 75th income percentile to a decrease of 43.1% (95% CI; 18.7–67.6) at the 25th income percentile. Conclusions It is important for healthcare and service providers to recognize the impact of stroke on return to prestroke levels of employment income. Low-income stroke survivors experience a more drastic loss in employment income and may need additional social support.

The agricultural sector and the forestry industry are important elements of the economies of many countries around the world. These two industries have many similarities that allow for comprehensive research that complement each other. The key quality of these industries is the use of biological resources, mostly of plant origin. In modern conditions, the agricultural and forestry sectors should use modern achievements of science and information technology. One of the most important elements of modern economics is the use of mathematical modeling tools. This study analyzes the possibilities of its application in the agricultural and forestry sectors (using the example of Russian practice). A theoretical analysis of the possibilities of using these tools in enterprises of the studied industries is presented. A practical study of the activities of Russian enterprises has been conducted. The factors influencing the development of the agricultural and forestry sectors are identified, and the degree of their influence on each of the industries is assessed. The approbation of the use of mathematical modeling tools and optimization of the enterprise's activities was carried out on the example of one of the logging enterprises. One of the important results was the determination of the features of software implementation at the enterprises of the agro-industrial complex and the forestry industry.

Given the expiration of expanded unemployment and other benefits during the COVID-19 pandemic, it is important to understand the association between pandemic income or job loss and long-term implications on mental health. To evaluate the association between income or job loss due to the COVID-19 pandemic and later psychological distress. This cohort study used 5 waves of nationally representative, longitudinal survey data (September 16, 2019, through September 18, 2022) from the Pew Research Center's American Trends Panel. Doubly robust propensity score-weighted quasi-Poisson models were used to estimate the association of self or household income or job loss during the early phase of the COVID-19 pandemic with later psychological distress, while controlling for pre-income loss characteristics (demographics, finances, and psychological distress). The study sample comprised US working-age adults (aged 18-64 years) who had not experienced income or job loss due to the COVID-19 pandemic by March 24, 2020. Participant-reported self or household income or job loss (ie, reduced hours or demand for work) due to the COVID19 pandemic between March 24, 2020, and August 16, 2020. Psychological distress was measured using a composite scale of 0 to 15 based on participants' reported frequency of feeling depressed, on edge, sleepless, lonely, and hopeless in the past week in March 2020, February 2021, and September 2022. Of 1392 working-age adults (survey weighted 52.7% male and 47.7% aged 30-49 years) who had not reported income or job loss before March 24, 2020, a survey weighted 35.7% reported job or income loss between March 24 and August 16, 2020. Early-phase pandemic income or job loss was associated with higher distress in February 2021 (estimated ratio, 1.09; 95% CI, 1.01-1.18; P = .03) and September 2022 (estimated ratio, 1.11; 95% CI, 1.02-1.22; P = .02) among participants who experienced job or income loss between March 24 and August 16, 2020, compared with the propensity score-weighted mean in the group who did not experience income loss. These small but significant within-person associations between early-phase pandemic household income or job loss and psychological distress up to 29 months later suggest that policies are needed to support people with income or job loss to help mitigate the long-term adverse mental health outcomes of economic disruption.

The IMPECAF is a research project that started at the end of 2018 and aims to deepen the knowledge on weather extremes, particularly droughts and heat waves, which affect agricultural and forest ecosystems of the Iberian Peninsula. Despite these events presenting different temporal scales, their simultaneous occurrence can intensify the observed impacts. In addition, these impacts may extend over large areas affecting different ecosystems. This project aims at transferring knowledge on fundamental research in meteorology for the agricultural and forestry sectors, and it is expected that the results may be an input in the decision-making process of farmers. To achieve this aim, appropriate measures will be developed to mitigate the impact of these extreme weather events in the forestry and agricultural sectors. This will be followed by an approach that ensures the involvement of stakeholders since the beginning of the project and even after its conclusion.

Understanding linkages between ecosystem service payments, forest plantations, and export agriculture

The many opportunities for mitigating atmospheric carbon emissions in developing countries include reforesting degraded lands, implementing sustainable agricultural practices on existing lands and slowing tropical deforestation. This analysis shows that over the next 10 years, 48 major tropical and subtropical developing countries have the potential to reduce the atmospheric carbon burden by about 2.3 billion tonnes of carbon. Given a central price of $10 per tonne of carbon and a discount rate of 3%, this mitigation would generate a net present value of about $16.8 billion collectively for these countries. Achieving these potentials would require a significant global effort, covering more than 50 million hectares of land, to implement carbon-friendly practices in agriculture, forest and previously forested lands. These estimates of host-country income potentials do not consider that outside financial investment may or may not be available. Our calculations take no account of the additional benefits of carbon sequestration in forest soils undergoing reforestation, increased use of biomass and reduced use of fossil-fuel inputs and reduced agricultural emissions. In all events, realizing these incomes would necessitate substantially greater policy support and investment in sustainable land uses than is currently the case.