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Human-induced climate change: the impact of land-use change

Abstract

For hundreds of years, human activity has modified the planet’s surface through land-use practices. Policies and decisions on how land is managed and land-use changes due to replacement of forests by agricultural cropping and grazing lands affect greenhouse gas emissions. Agricultural management and agroforestry and the resulting changes to the land surface alter the global carbon cycle as well as the Earth’s surface albedo, both of which in turn change the Earth’s radiation balance. This makes land-use change the second anthropogenic source of climate change after fossil fuel burning. However, the scientific research community has so far not been able to identify the direction and magnitude of the global impact of land-use change. This paper examines the effects of net carbon flux from land-use change on temperature by applying Granger causality and error correction models. The results reveal a significant positive long-run equilibrium relationship between land-use change and the temperature series as well as an opposing short-term effect such that land-use change tends to lead to global warming; however, a rise in temperature causes a decline in land-use change.

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Notes

  1. 1.

    Based on the global carbon model EFF + ELUC = GATM + SOCEAN + SLAND + BIM, where EFF is carbon dioxide emissions from fossil fuel and industry, ELUC represents emissions from land-use change, GATM is the growth rate of global atmospheric carbon dioxide concentration, SOCEAN and SLAND represent the ocean and terrestrial carbon dioxide sinks and BIM is a mismatch measure, the Global Carbon Project (2017) reports emission estimates of 9.4 ± 0.5 GtC yr.−1 for EFF, 1.3 ± 0.7 GtC yr.−1for ELUC, 4.7 ± 0.1 GtC yr.−1 for GATM, 2.4 ± 0.5 GtC yr.−1 for SOCEAN and 3.0 ± 0.8 GtC yr.−1 for SLAND between 2007 and 2016.

  2. 2.

    For each temperature type (with the exception of sea surface temperature) datasets were additionally derived from different sources and serve as sort of sensitivity analysis and a safeguard against inaccuracies in the data and biased results.

  3. 3.

    According to Alexander (1999) the Engle-Granger test is best suited for testing longer time series in a bivariate setting (Alexander 1999) and in such cases should be favored over the cointegration test developed by Johansen (1988).

  4. 4.

    Including a deterministic trend would violate the idea of cointegration since errors would increase over time.

  5. 5.

    Note that model selection criteria, such as the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC), can be used to determine the appropriate model order p.

  6. 6.

    Although we can identify a long run relationship for the CDIAC temperature series and net carbon flux from land-use change, the results differ for the temperature series from the NASA GISS and the NCDC NOAA. These differences may be due to the different examination periods. While the CDIAC data are available from 1850, the remaining temperature series start in 1880.

  7. 7.

    While our findings are data driven econometric estimates, the findings of Werth and Avissar (2005), Zhao et al. (2001), Chase et al. (2000) and Findell et al. (2007) are climate model based simulations of a change between naturel to present land cover or simulations of complete deforestation. Further, it should also be noted that although these studies can identify little or no global effect, they find local effects and statistically significant changes in regional temperature.

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Gries, T., Redlin, M. & Ugarte, J.E. Human-induced climate change: the impact of land-use change. Theor Appl Climatol 135, 1031–1044 (2019). https://doi.org/10.1007/s00704-018-2422-8

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