Impact Of Living Standards On Dry Forest

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Impact of Living Standards on Dry Forest Degradation

Tribal and Marginalized Households in Central Indian Highlands

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About this dataset

This dataset gives researchers a comprehensive view into the vital connections between poverty, land use, and forest degradation in dry tropical forests of the Central Indian Highlands. To help quantify the impact of two interventions - improved living standards (eg. alternatives to wood for cooking and housing materials) - on forest health, it provides information on several factors including biodiversity indices, tree cover data, distance from roads and cities, number of cattle owned by households, usage patterns of fuelwood/construction material gathered from forests, and other critical metrics. With this in-depth set of data points combined with remotely sensed forest degradation information available here through 5000 household surveys of forest use case studies, users can gain critical insight into how effective these interventions can be in improving human well-being as well as conserving pine dry tropical forests. Explore this dataset to learn more about how you can act on ways to improve living conditions for local populations while reducing major deforestation trends!

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How to use the dataset

In order to make the most of this dataset, researchers should utilize the data in several ways.

First, researchers can use this dataset to explore how household living standards affect forest degradation in dry tropical forests of the Central Indian Highlands. Specifically, they should investigate how two promising interventions – providing alternatives to fuelwood for cooking and nonthe-forest based housing materials – impact forest degradation. The data provides a comprehensive view into the relationship between various factors like poverty, fuelwood use, and land use on trees located within five kilometers of a household.

Second, researchers can also apply multiple regression analysis techniques in order to gain an understanding about which factors have the strongest relationship with forest degradation at both 1 km and 5 km distances from households. These regressions would give key insight into how changes in livelihoods due improved living standards affected change over time throughout these areas.

Thirdly, researchers can utilize quantitative data collected from 5000 households across 10 villages located in 3 states of central India's highlands allowing them to develop predictive models relating certain measured impacts with possible future outcomes across those areas as well as other similar regions involving tribal populations that are heavily dependent on forests for their everyday needs such as fuelwood collection or construction purposes amongst others.

Finally Researchers could also compare similar interdisciplinary studies related to other environmental issues such as climate change or air pollution and whether any correlations arise from looking at dynamic interactions between these natural phenomena occurring together at once and potentially shed light on ways better provisioning services or management practices might be implemented towards tackling climate issues more effectively leading to better natural landscapes around us today!

Research Ideas

• Measuring the effectiveness of fuel-wood alternatives on forest degradation levels in this region.
• Quantifying the effects of poverty on land use decision making and forest dependence.
• Developing models to predict deforestation risk based on a combination of socio-economic factors, such as household income, accessibility to services and infrastructure, access to alternative sources of energy, and access to conservation programs or incentives incentivizing sustainable land use practices

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: data_post.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .