Allocating and Mapping Carbon Footprint at the Township Scale by Correlating Industry Sectors to Land Uses


Geographical Review 106(3)

Chengliang Liu, Tao Wang, Xiao Lin and Rui Zhao



American Geographical Society: What is the main purpose of your study?

Chengliang Liu: The purpose of this study is two-fold. One is to seek an improved method for allocating larger-scale carbon footprints to local authorities by using land use as a weight. The second is to offer insight into mapping spatiotemporal variations in carbon footprints at the township scale and reveal some potential factors through correlation analysis.


American Geographical Society: What are the practical, day to day implications of your study?

Chengliang LiuThis provides a technical framework for abatement and allocation of carbon emissions from global or national levels to local authorities. Specifically, based on the IPCC’s inventories, global energy-based carbon emissions are distributed to each country/region through intergovernmental negotiation. In light of the land use weight provided by RS/GIS, national or regional targets and quotas are distributed and visualized at local or smaller scales such as urban, county, and even neighborhood levels. In doing so, it is expected that this simple but efficient approach is more facilitating for governmental policy-makers to implement CO2 emissions abatement and reallocation plans according to local GR articleconditions.


American Geographical Society: How does your study relate to other work on the subject?

Chengliang Liu: Hardly any such spatial allocative framework provides simple and efficient allocations of carbon footprints from a down-scaled perspective (from larger scales to smaller ones), because of uncertainties and inadequate data at smaller scales. This article improves upon an IPCC approach to spatially allocating and displaying carbon footprints by integrating the IPCC’s assessment and RS-based land use classification, and attempts to address the few answered question.


American Geographical Society: What are two or three interesting findings that come from your study?

Chengliang LiuThere is a long-term, stable, consistent, and equilibrium relationship between industrial sectors and land use demonstrated by a multitude of co-integration tests. Meanwhile, an increasing local difference in carbon footprints is depicted in a core-periphery pattern; that is, those towns belonging to or close to the center have higher footprints than peripheral mountains areas. This configuration is closely related to the regional distribution of population size, infrastructure location, and built-up area. In those regions along transport corridors with larger populations and built-up areas, their carbon footprints are always greater due to the larger amount of energy consumption.


American Geographical Society: What might be some of the theoretical implications of this study?

Chengliang LiuThere is a difficult but key question: how should national targets for carbon footprint reduction be reallocated to local authorities? We hope that this analysis encourages other geographers to not only visualize carbon footprints but also efficiently allocate them at local or smaller scales, especially in fast-growing China and other developing countries.


American Geographical Society: How does your research help us think about Geography?

Chengliang LiuDuring inter-governmental climate negotiations, the mitigation and allocation of carbon footprints has been a critical issue and task for policy-makers to implement allocative targets and policies. These depend intensively on statistically and spatially efficient estimates. How should they estimate their own carbon footprints? How should they reallocate national carbon footprints to local authorities? Who should be responsible and where? How much should they take responsibility for? What areas should they concentrate on? How can they change? What factors are relevant? These questions, which have been asked by geographers for quite some time, are rarely addressed systematically and efficiently, notably at smaller scales.  Hopefully, this article helps people think of the statistical and spatial allocations of carbon footprints at local to neighborhood scales.

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