Macroecology Meets Macroeconomics: Resource Scarcity and Global Sustainability

Abstract

The current economic paradigm, which is based on increasing human population, economic development, and standard of living, is no longer compatible with the biophysical limits of the finite Earth. Failure to recover from the economic crash of 2008 is not due just to inadequate fiscal and monetary policies. The continuing global crisis is also due to scarcity of critical resources. Our macroecological studies highlight the role in the economy of energy and natural resources: oil, gas, water, arable land, metals, rare earths, fertilizers, fisheries, and wood. As the modern industrial technological-informational economy expanded in recent decades, it grew by consuming the Earth's natural resources at unsustainable rates. Correlations between per capita GDP and per capita consumption of energy and other resources across nations and over time demonstrate how economic growth and development depend on "nature's capital". Decades-long trends of decreasing per capita consumption of multiple important commodities indicate that overexploitation has created an unsustainable bubble of population and economy.

Keywords: Ecological economics; Economic growth; Global sustainability; Human ecology; Macroecology; Resource scarcity.

Fig. 1

Relationships between per capita energy use and per capita GDP: a) Across countries, with each point representing the average energy consumption and the average GDP from 1980–2005; b) over time, with each arrow showing the net change from 1980 to 2005. Note that per capita energy consumption scales as the 0.76 power of GDP (a), and the changes in energy consumption over the 25 years (b) parallels this scaling relationship. Replotted using data compiled by Brown et al. (2011).

Fig. 1

Relationships between per capita energy use and per capita GDP: a) Across countries, with each point representing the average energy consumption and the average GDP from 1980–2005; b) over time, with each arrow showing the net change from 1980 to 2005. Note that per capita energy consumption scales as the 0.76 power of GDP (a), and the changes in energy consumption over the 25 years (b) parallels this scaling relationship. Replotted using data compiled by Brown et al. (2011).

Fig. 2

Annual percent change in GDP and resource consumption for the US and China from 2000 to 2010. China’s economic growth of more than 15% per year was accompanied by commensurate increases in consumption of energy, water, metals, phosphate, and fisheries. Much slower growth of the US economy consumed much less of all these resources. Some of the changes in individual commodities also reflect trends due to globalization. For example, the shift in manufacture and export of electronics from the US to China is reflected in the decrease copper consumption in the US and the large increase in China.

Fig. 3

Trajectory of per capita extraction and consumption of natural resources since the 1960s. Note that per capita supplies of all these resources, except for iron and possibly molybdenum and cement, have peaked, often decades ago, and are now declining. Data sources: per capita values represent the total values divided by global population size as reported by the World Resources Institute (http://earthtrends.wri.org/). Individual sources for global production/consumption values are as follows: Agricultural land in km² is from the World Development Indicators Database of the World Bank (http://data.worldbank.org/data-catalog/world-development-indicators) and represents the sum of arable, permanent crop, and permanent pasture lands. Freshwater withdrawal in km³ from 1960, 1970, 1980, and 1990 is from UNESCO http://webworld.unesco.org/water/ihp/db/shiklomanov/part%273/HTML/Tb_14.html) and for 2000 from The Pacific Institute (http://www.worldwater.org/data.html). Wild fisheries harvest in tonnes is from the FAO Fishery Statistical Collection Global Capture Production Database http://www.fao.org/fishery/statistics/global-capture-production/en) and is limited to diadromous and marine species. Wood building material production in tonnes is based on the FAO ForeSTAT database (http://faostat.fao.org/site/626/default.aspx), and represents the sum of compressed fiberboard, pulpwood+particles (conifer and non-conifer [C & NC]), chips and particles, hardboard, insulating board, medium density fiberboard, other industrial roundwood (C & NC), particle board, plywood, sawlogs+veneer logs (C & NC), sawn wood (C & NC), veneer sheets, and wood residues. Phosphate, copper, molybdenum, pig iron, gold, and combustible coal production data in tonnes is based on World Production values reported in the USGS Historical Statistics for Mineral and Material Commodities (http://minerals.usgs.gov/ds/2005/140/). Global coal production data is limited to 1966–2008. Petroleum production in barrels from 1965 to 2008 is based on The Statistical Review of World Energy http://www.bp.com/sectiongenericarticle800.do?categoryId=9037130&contentId=7068669) and represents all crude oil, shale oil, and oil sands plus the liquid content of natural gas where this is separately recovered. These data are reported in 1,000 barrels/day, and were transformed to barrels per capita per year. GDP in 1990 US dollars are from the World Resources Institute (http://earthtrends.wri.org/). All data were accessed May 2011 to October 2012. After Burger et al. (2012) with new graphs for iron, molybdenum, and gold added.

Fig. 4

Variation across countries in relationships between GDP and variables that reflect standard of living and quality of life. First row: overall standard of living: A) Human Development Index (HDI), B) per capita wealth, C) poverty; second row: D) health: infant mortality, E) doctors, F) calories in diet; third row: technology: G) cars, H) cell phones, I) internet users; fourth row: education and research: J) secondary education, K) research spending, L) patents. In all cases each data point represents the value for a country, GDP is scaled logarithmically and plotted on the x-axis, the other variables are either log transformed or not, depending on which gives better fit, and correlation coefficients are given. Variables are either per capita or per hundred or thousand population as in the original source. Note that all variables are well correlated with GDP per capita, although the goodness of fit and exact form of the relationships vary. Data from http://databank.worldbank.org/data/home.aspx, accessed May 2011 to October 2012.