Environmental Science & Technology POLICY ANALYSIS Table 1. Wald F-Test Statistics from Panel-Based Vector Error Correction Modela Table 2. Hurlin Heterogeneous Panel Granger Causality Test Resultsa F-statistic value panels causal result short run long run U1 BU pGDP 3.46 (0.06) 210.15 (0.00) pGDP BU 0.14 (0.71) 30.79 (0.00) U2 BU pGDP 60.07 (0.00) 571.59 (0.00) pGDP BU 15.42 (0.00) 147.01 (0.00) U3 BU pGDP 32.39 (0.00) 462.80 (0.00) pGDP BU 1.93 (0.07) 51.96 (0.00) P BU pGDP 14.93 (0.00) 45.09 (0.00) pGDP BU 3.23 (0.02) 57.78 (0.00) a The results suggest the existence of long-run bidirectional causalities between built-up area and GDP per capita in all panels; no short-run causality in panel U1; bidirectional short-run causalities in panel U2 and panel P; short-run causal eﬀect from built-up area to GDP per capita in panel U3. Note: Panel U1 consists of 174 Chinese cities’ annual data during 1990 1998. Panel U2 consists of 135 Chinese cities’ annual data during 1994 2005. Panel U3 consists of 121 cities that appear in both U1 and U2 panel, annual data during 1990 2005. Panel P consists of 31 Chinese provinces’ annual data during 1997 2006. BU and pGDP stand for built-up area and GDP per capita, respectively. The hypothesis is non-causality. Cases with probability levels (shown in parentheses) lower than 0.05 reject the hypothesis. Hurlin’s method, the causal eﬀects in U2 panel are not signiﬁcant, and causal eﬀect from GDP per capita to built-up area in panel U3 only under Lag2 model (Table 2). Summarizing the results, we can draw two conclusions. First, there is a long-run bidirectional causality between GDP per capita and built-up area in all panels. The three urban panel results suggest that there is a positive feedback, where urban land expansion brings about growth in GDP per capita which in turn increases urban land expansion. This positive feedback may explain the mechanism behind the trends in Figure 3, indicating an accelerating growth in GDP per capita with the growth in the city size. The provincial panel consists of total urban land expansion within the province and overall GDP per capita of the province including city and rural, so the result at the provincial panel means urban land expansion not only contri- butes to the city GDP, but also to the overall GDP growth of the province. This means the eﬀect of urban expansion extends beyond the city boundary, and has spillover inﬂuences to the region surrounding the city. Second, our results indicate that the provincial panel has short-run bidirectional causality but do not suﬃciently conﬁrm a short-run relationship in any of the three urban panels. There are several possible reasons for this. A careful study of the original data suggests that city level built-up area data some- times increase abruptly due to the eﬀect of large built-up area projects, while at the provincial level these steps are smoothed out by multiple cities. Since the short-run relationship measures year-to-year variation, this diﬀerence in data trends can cause the lack of a short-run causal eﬀect. In addition, our urban panels consist of cities that have a full range of data and thus exclude cities that have become prefectural level cities after 1990 (in U1 and U3) (for example, there were 31 new pre- fectural level cities between 1990 and 1997) or 1997 (in U2) and small cities and towns below prefectural level, while the provincial panel naturally includes all cities within the province panel causal result Z~NH,NTC U1 ΔBU ΔpGDP Lag1: ΔpGDP ΔBU Lag1: U2 ΔBU ΔpGDP Lag1: 1.04 (0.30) Lag2: ΔpGDP ΔBU Lag1: 0.28 (0.78) Lag2: U3 ΔBU ΔpGDP Lag1: 0.04 (0.97) Lag2: 1.63 (0.10) Lag3: 1.20 (0.23) Lag4: ΔpGDP ΔBU Lag1: 0.57 (0.58) Lag2: 2.07 (0.04) Lag3: 1.86 (0.06) Lag4: P ΔBU ΔpGDP Lag1: 2.34 (0.02) Lag2: ΔpGDP ΔBU Lag1: 2.04 (0.04) Lag2: a The results indicate a bidirectional short-run causality between built-up area and GDP per capita in panel P, and a causal eﬀect from GDP per capita to built-up area in panel U3 only under Lag2 model. Note: Panel U1 consists of 174 Chinese cities’ annual data during 1990 1998. Panel U2 consists of 135 Chinese cities’ annual data during 1994 2005. Panel U3 consists of 121 cities that appear in both U1 and U2 panel, annual data during 1990 2005. Panel P consists of 31 Chinese provinces’ annual data during 1997 2006. BU and pGDP stand for built-up area and GDP per capita, respectively. Δ denotes the ﬁrst diﬀerence of the variable. Z~HN,NTC is a statistic deﬁned by Hurlin34. Lag1, Lag2, Lag3, and Lag4 mean Hurlin’s test model of lag order 1, 2, 3, and 4, respectively. The hypothesis is homogeneous non-causality. Cases with prob- ability levels (shown in parentheses) lower than 0.05 reject the hypothesis. regardless of their times of emergence. It is likely that these new prefecture level cities and small towns and cities below pre- fectural level have contributed toward a stronger short-run causal eﬀect at the provincial level. ’ DISCUSSIONS The Role of Land in China’s Economic Growth. Land is regarded as playing a less and less important role in econom- ic growth.44 46 Our findings in China suggest otherwise. China’s legal system on land ownership and land-use rights might con- tribute to the significant role of landscape urbanization in eco- nomic growth and the positive feedback between them in recent decades. Land is publicly owned in China, and land supply becomes a powerful macro-economy intervention tool for Chinese governments especially since 2003.47 China has a system of long-term lease of land-use rights, which means government could issue a one-off land lease for a duration of 50 70 years,48 and raise considerable revenue from the leasing. During the six year period of 1999 to 2006, the total income arising from land-use leasing of state-owned land, which often means con- verting other land-use types into urban built-up land, grew ∼15 times, reaching 808 billion Yuan,49 raising its proportion in China’s government financial income ∼3.6 times, reaching 136 dx.doi.org/10.1021/es202329f |Environ. Sci. Technol. 2012, 46, 132–139