Editorial Feature

Understanding Vegetation Changes in Coal Mining Areas

Scientists have developed a modeling system to study the relationships between anthropogenic activity and natural factors, in relation to vegetation cover in the Yangquan coal mining area of China. Natural factors were found to have a positive impact on fractional vegetation cover over time, whereas human activity negatively drives fractional vegetation cover. These are important indicators for assessing the ecological health of the environment where mining occurs, and how land recovers after it has been exploited.

coal mining, mining

Image Credit: Parilov/Shutterstock.com

China’s Economic Dependence on Coal Mining

China’s economy is heavily reliant on coal mining. Its coal production and consumption are the highest in the world, producing over 3.84 billion tons and consuming 3 billion tons in 2020, more than half the world’s coal. This makes China the biggest contributor to anthropological climate change.

China provided half of all overseas public finance for coal-fired plants between 2013-18. Then, in September 2021, President Xi Jinping announced China has abandoned plans to build more plants abroad, although exactly when and how this policy comes into play remains unclear, as it still has major financial overseas commitments in existence.

As of 2022, China has 1,110 coal fire power stations in operation, 4 times the number of India, ranked second. 

China has also suffered fluctuating coal prices and emergency situations affecting the grid.

In July 2021, dozens of mines were temporarily shut down due to severe flooding in Shanxi Province, which killed 15 people, but then the Government ordered output to be increased, after 1.76 million homes, and manufacturing suffered major power cuts, and factories had to stop production.

Impact of Burning Coal in China

Severe health problems affect millions, from exposure to the toxic chemicals released by burning coal.

Asthma, cardiovascular disease, and strokes are believed to account for between 700,000 to 2.2 million premature deaths in China every year. Millions more live with chronic conditions such as lung cancer, Bowen’s disease from arsenic exposure, selenosis, arsenosis, fluorosis, and black lung disease. Women are adversely affected by miscarriages, and premature births and birth defects are a common occurrence.

People are also deeply concerned about the ecological impact on the Chinese landscape, which has resulted in a number of policies to address it, including land reclamation, ecological restoration and afforestation.

Vegetation Fractional Cover

Vegetation Fractional Cover (VFC) is a measurement that splits the land into three parts; green, brown and bare ground. 

Green is grass, crops and leaves, brown is branches, hay, dead leaf litter, and bare ground refers to soil and rock. 

VFC is used to assess, monitor and make recommendations based on the health of the natural environment through modeling, study and mapping.  

Anthropogenic factors such as gross domestic product (GDP), density and population density are often ignored in VFC research. However, with the increasing development of society and economy, it becomes an increasing necessity to factor in human activity upon vegetation, and what either drives degradation or improves it.

Geographical Weighted Regression Model

A Geographical Weighted Regression Model (GWR) was developed by scientists using VFC to assess the impact of mining on China’s environment. It used Yangquan as a case study because it contains the biggest and most important anthracite coal production in China.

The study looked at areas being mined, and areas with coal resources, but not yet being mined, and asked what the differences in vegetation cover are. Secondly, it asked whether the dominant driving factors for any changes were natural,  anthropogenic or distance:

  • Natural is defined as temperature, precipitation, slope and elevation.
  • Anthropogenic is defined as population density and GDP density.
  • Distance is defined as the distance from road, town, and water source.

The study took place using four time periods; 2000, 2005, 2010, and 2015, and also considered topography such as slopes and roads.

Results of Weighted Regression Model

The GWR model revealed anthropogenic factors were the dominant driving factors of VFC degradation, followed by natural factors, and lastly to a negligible extent, the distance. It also revealed exploited areas used for mining were worse than unexploited areas.

Results showed GDP provided a negative result, natural causes provided a positive result, but when combined suggest a negative influence overall, whereby a combination of anthropogenic and natural factors have degrading effects. 

The results also indicated each different time period produced similar results.

Interestingly, natural factors had a small positive impact intensity on VFC, whilst anthropogenic factors had a more intensive negative impact on the exploited mined areas.   

Natural and anthropogenic factors acting together create a phenomenon whereby vegetation in the Yangquan mining area increased over 18 years from 3.92% to 8.48%, but is higher in unexploited areas. The rate of vegetation recovery was significantly higher in exploited areas than unexploited areas, but when accompanied by urban development, created a negative impact.

Overall, VFC in the Yangquan mining area represents an upward trend from 1998 to 2015, consistent with other research, that global vegetation shows increasing leaf area from 2000 to 2017, especially in China and India. 

The study revealed land management, climate change, CO2 fertilization, nitrogen deposition, and land disturbance recovery, are mainly responsible for driving global greening.   

However, anthropogenic factors such as GDP had a high negative impact.

It also showed that GDP and population factors alone, do not fully represent the impact of human activity on VFC. Coal mining intensity, together with geological differences, and how vegetation has been used historically, may impact results. Therefore, VFC in exploited areas with more human activity cannot be relied upon using GWP modeling alone.

Afforestation and other land recovery projects were found to have significant beneficial outcomes on VFC.

The study concluded that anthropogenic factors are hugely complex and difficult to quantify, and therefore although GWR modeling is useful, more factors need to be considered to provide advanced quantitative methods in the future.  

References and Further Reading

Vegetation changes in coal mining areas: Naturally or anthropogenically Driven? (Jan 2022) Chen. L, Hong. Z, Zhang. X, Liu. P, Wanchang. Z, Ma. X in CATENA Volume 208, in Elsevier, in Science Direct online https://www.sciencedirect.com/science/article/pii/S0341816221005701

How China Shapes the World’s Coal (11.03.2021) Early. C. In BBC ‘towards net zero’ online https://www.bbc.com/future/article/20211028-how-chinas-climate-decisions-affect-the-world 

Global operational coal-fired power stations by country 2022. Sonnichsen.N in Statistica online (accessed 02.18.2022) https://www.statista.com/statistics/859266/number-of-coal-power-plants-by-country/

U.N climate agreement cliched after late drama over coal (11.14.2021) Volcovici, V, Abnett. K, James. W in Reuters Online. https://www.reuters.com/business/cop/un-climate-negotiators-go-into-overtime-save-15-celsius-goal-2021-11-13/

China floods: coal price hits fresh high as mines shut (10.12.2021) in BBC News online. https://www.bbc.co.uk/news/business-58879481

The health impacts of coal use in China (2017, June) Finkelman. R.B, Tian. L in ResearchGate online. https://www.researchgate.net/publication/317926191_The_health_impacts_of_coal_use_in_China

DEA Fractional cover (Landsat) (09.13.2016) in Australian Government Geoscience Australia online. https://cmi.ga.gov.au/data-products/dea/119/dea-fractional-cover-landsat-deprecated

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Georgie Lyng

Written by

Georgie Lyng

Georgie Lyng is a freelance writer, with a strong interest in environmental issues, a focus on sustainable technologies, climate change science, improving biodiversity, and protection of natural ecosystems. Georgie completed an Open University BSc Environment Studies degree in 2016, enjoys researching environment issues, and writing about the latest scientific developments in the industry and sustainable solutions to help protect the environment.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Lyng, Georgie. (2022, March 14). Understanding Vegetation Changes in Coal Mining Areas. AZoMining. Retrieved on April 19, 2024 from https://www.azomining.com/Article.aspx?ArticleID=1645.

  • MLA

    Lyng, Georgie. "Understanding Vegetation Changes in Coal Mining Areas". AZoMining. 19 April 2024. <https://www.azomining.com/Article.aspx?ArticleID=1645>.

  • Chicago

    Lyng, Georgie. "Understanding Vegetation Changes in Coal Mining Areas". AZoMining. https://www.azomining.com/Article.aspx?ArticleID=1645. (accessed April 19, 2024).

  • Harvard

    Lyng, Georgie. 2022. Understanding Vegetation Changes in Coal Mining Areas. AZoMining, viewed 19 April 2024, https://www.azomining.com/Article.aspx?ArticleID=1645.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this article?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.