Environmental Science & Technology POLICY ANALYSIS Figure 1. Flowchart of causality test. conducting cross-country time-series analysis, using either statistical data or satellite remote sensing data. In addition to describing urbanization processes in general, landscape urban- ization indicators may reveal linkages between urbanization and economic and ecological processes and environmental conse- quences that demographic data cannot.13,19 21 Many studies argue that economic growth stimulates urban expansion in China or vice versa, but these arguments are generally based on a positive correlation between these two indicators.22 25 Yet, correlation does not necessarily mean causation. Recent study on land-use drivers in the Pearl River Delta during 1988 199626 found a causal linkage from foreign direct investment to urban expansion but not vice versa. Du et al.27 found a bidirectional causality between urban land expansion and economic growth in an economic and techno- logical development zone in Tianjin. Yet, these studies focus on speciﬁc city region or urban districts, and there is no analy- sis illuminating the causal relationships between urban land expansion and economic growth across a range of Chinese cities and regions. In this work, we re-examine the causal relationship between urbanization and economic growth in the Chinese context, using built-up area as a landscape urbanization indicator. We assess patterns of urban land expansion and economic growth according to diﬀerences in city size, conduct long-term and short-term causal analysis between urban land-use expansion and economic growth, both at city level and provincial level. We end with a brief discussion on policy and scientiﬁc implications of our results. ’ DATA AND METHODS To examine whether landscape urbanization boosts econom- ic growth at the provincial and urban level, and whether economic growth causes further landscape urbanization, we examine how built-up area and economic growth changed in diﬀerent cities, and conduct causality analysis between built-up area and GDP per capita. In this paper, we use the deﬁnition of causality proposed by Granger,28 where variable X is said to be the Granger cause of Y if at time t, Yt+l is better predicted by using past values of X than by not doing so. Our causality test consists of four stages (see Figure 1 for the ﬂowchart): (1) heterogeneous panel unit root tests29,30 to test the order of integration of variables; (2) Pedroni’s panel cointegration test31,32 to examine the long-run relationship between the variables given that all variables are ﬁrst-order integrations; (3) panel-based vector error correction model (VECM)33 to examine both short- and long-run causality; and (4) heteroge- neous panel causality test34 on the ﬁrst diﬀerence of all variables to further verify short-run causality. Decisions about statistical signiﬁcance of test were made on a 5% level. As previously discussed, we use built-up area as indicator of landscape urbanization. In China, built-up area is deﬁned as a largely continuous area covered by urban facilities but may include some isolated facilities such as airports. It is generated by the Ministry of Housing and Urban-Rural Development (MOHURD) based on two sources: (i) annual incremental data based on the actual land area that is granted the permission to be developed into urban land in each city each year; (ii) State Bureau of Surveying and Mapping performs actual measure- ments annually or every other year, integrating aerial photo 133 dx.doi.org/10.1021/es202329f |Environ. Sci. Technol. 2012, 46, 132–139