# Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Expanded Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) Carbon Emission Model

^{b}A

^{c}T

^{d}e

_{1}(ln P) + β

_{2}(ln UR) + β

_{3}(ln GDPPC) + β

_{4}(ln EI) + β

_{5}(ln IS) + β

_{6}(ln ECS) + β

_{7}ln(EP) + β

_{8}ln(OPEN) + ln e = α + β

_{1}(ln P) + β

_{2}(ln UR) + β

_{3}(ln GDPPC) + β

_{4}(ln EI) + β

_{5}(ln IS) + β

_{6}(ln ECS) + β

_{7}(ln EP) + β

_{8}ln(OPEN) + ε

_{1}(ln P) + β

_{2}(ln UR) + β

_{3}(ln GDPPC) + β

_{4}(ln EI) + β

_{5}(ln IS) + β

_{6}(ln ECS) + β

_{7}ln(EP) + β

_{8}ln(OPEN) + ln e = α + β

_{1}(ln P) + β

_{2}(ln UR) + β

_{3}(ln GDPPC) + β

_{4}(ln EI) + β

_{5}(ln IS) + β

_{6}(ln ECS) + β

_{7}(ln EP) + β

_{8}ln(OPEN) + ε

_{2}emission per unit of GDP, measured by in 10,000 Yuan/ton), P represents population (population size, in 10,000), UR denotes the urbanization level (measured by the percentage of urban population in total population), GDPPC denotes economic development (measured by GDP per capital), EI is energy intensity (measured by energy consumption per unit of GDP), IS represents industrial structure (measured by the secondary industry share of GDP), ECS represents energy consumption structure (measured by the percentage of coal in total energy consumption), EP is energy price (measured by producer’s price index for manufactured products), and OPEN is openness (measured by the gross investment the registered foreign-funded enterprises by region at the year-end).

## 3. Construction of Carbon Emission Spatial Econometric Model and Spatial Weight Matrix

_{2}emissions per unit of GDP by 17 percent in 2015 compared to 2010. Plans to reduce CO

_{2}emissions need to be included by the regions in their plans for economic and social development and the yearly plan, making carbon emissions an important indicator of development. In addition, in 2011, the National Development and Reform Commission issued Notice Regarding the Development of Carbon Emissions Trading Pilot, whose aim is to implement a pilot trading of carbon emissions rights in seven provinces, including Beijing, Tianjin, Shanghai, Chongqing, Guangdong, Hubei, and Shenzhen. These regions are required to draft a regulation and formulate the rules for the pilot carbon emissions trading and to set a regional GHG emissions reduction target. This makes the scale of carbon emissions an important monitoring indicator of regional carbon emissions. In line with this, this paper uses two variables—the scale and intensity of carbon emissions—as independent variables to obtain a comprehensive result.

#### 3.1. Spatial Lag Panel Data Model (SLPDM)

_{it}and InCI

_{it}denote scale and intensity of carbon emissions of the ith region at time t, respectively. Σw

_{ij}InCS

_{it}and Σw

_{ij}InCI

_{it}represent the spatial correlation between InCS

_{it}and InCI

_{it}of the region i and that of its adjacent regions. InP

_{it}, InUR

_{it}, InGDPPC

_{it}, InEI

_{it}, InIS

_{it}, InECS

_{it}, InEP

_{it}and InOPEN

_{it}are independent variables of region i at time t. δ is spatial auto-correlation index, w

_{ij}is an element of the spatial weight matrix representing the spatial relations between region i and j. α is the constant term, β is are coefficients to be estimated, μ

_{i}is the individual-fixed effect and λ

_{t}is the time-fixed effect.

#### 3.2. Spatial Error Panel Data Model (SEPDM)

_{it}, InCI

_{it}, α, β, μ

_{i}, λ

_{t}, ε

_{it}, InP

_{it}, InUR

_{it}, InGDPPC

_{it}, InEI

_{it}, InIS

_{it}, InECS

_{it}, InEP

_{it}and InOPEN

_{it}are defined same as in (6) and (7). ϕ

_{it}denotes spatial error auto-correlation, ρ is spatial auto-correlation index.

#### 3.3. Spatial Durbin Panel Data Model (SDPDM)

_{it}, InCI

_{it}, InP

_{it}, InUR

_{it}, InGDPPC

_{it}, InEI

_{it}, InIS

_{it}, InECS

_{it}, InEP

_{it}, InOPEN

_{it}, α, μ

_{i}, λ

_{t}are defined the same as in (6) and (7). θ is a vector of coefficients to be estimated. We test the hypothesis H

_{0}: θ = 0 and H

_{0}: θ + δβ = 0. The reject of the hypothesis indicates that SDPDM best fits the data.

#### 3.4. Spatial Weight Matrix

_{ij}is the distance between regional i and j, which is calculated from their longitudes and latitudes.

_{it}denotes per capita GDP, representing the income level of region i at time t (Deflated by the price index in 2006). Thus, W* incorporates economic development into the weight matrix. α is the adjustable parameters of economic weight, usually 1 or 2. When two different geo-spatial locate at the same level of economic development with the same period of time, the denominator is zero. To avoid the zero distance problem, when there are any two different locations in the same period geospatial economic variables are the same, m take 1. When any two different locations in the same period geospatial economic variables are not the same, m is taken to 0.

## 4. Model Selection

Variable | Pooled ML | Individual-Fixed Effects | Time-Fixed Effects | Individual and Time-Fixed Effects |
---|---|---|---|---|

Constant | –3.3551 *** (–5.8748) | —— | —— | —— |

Log(P) | 0.9307 *** (21.2903) | 0.7383 *** (3.9259) | 0.9269 *** (21.0747) | 1.2101 *** (4.5873) |

Log(UR) | –0.2600 (–1.2142) | 0.1818 (0.8779) | –0.1983 (–0.8826) | 0.4890 ** (2.2189) |

Log(GDPPC) | 0.8992 *** (9.6115) | 0.9090 *** (8.0765) | 0.8583 *** (8.2249) | 1.1905 *** (6.6064) |

Log(EI) | 1.1225 *** (15.1500) | 1.1579 *** (4.6128) | 1.1284 *** (15.1838) | 1.1928 *** (4.2843) |

Log(IS) | 0.1321 (1.0852) | 0.2492 ** (2.2930) | 0.1208 (0.9918) | 0.1563 (1.3847) |

Log(ECS) | 0.2847 *** (3.4521) | 0.2583 *** (2.6862) | 0.2901 *** (3.5199) | 0.2952 *** (3.0436) |

Log(EP) | –0.0611 (–0.2379) | –0.1480 ** (–2.2421) | 0.3289 (0.6455) | 0.0590 (0.4786) |

Log(OPEN) | 0.0804 ** (2.4207) | 0.0556 *** (2.7728) | 0.0895 *** (2.6282) | 0.0554 *** (2.6780) |

σ^{2} | 0.0069 | 0.0004 | 0.0068 | 0.0003 |

R^{2} | 0.9432 | 0.8341 | 0.9431 | 0.4882 |

Adjusted R^{2} | 0.9400 | 0.8259 | 0.9403 | 0.4630 |

Durbin–Watson | 2.0312 | 1.8704 | 2.0661 | 1.9776 |

Log-Likelihood | 164.6880 | 382.5538 | 165.5679 | 390.1138 |

LM Spatial Lag | 25.6397 (0.000) | 6.9517 (0.008) | 25.0131 (0.000) | 8.5919 (0.003) |

Robust LM Spatial Lag | 31.6000 (0.000) | 3.6978 (0.054) | 31.2468 (0.000) | 0.3639 (0.546) |

LM Spatial Error | 1.1145 (0.291) | 3.4387 (0.064) | 1.3975 (0.237) | 9.5364 (0.002) |

Robust LM Spatial Error | 7.0748 (0.008) | 0.1847 (0.667) | 7.6311 (0.006) | 1.3083 (0.253) |

Joint Test of Significance LR | Fixed-Effects | Statistics | df | p-value |

Individual-Fixed Effects | 449.0918 | 30 | 0.0000 | |

Time-Fixed Effects | 15.1200 | 5 | 0.0099 |

Variable | Pooled ML | Individual-Fixed Effects | Time-Fixed Effects | Individual and Time-Fixed Effects |
---|---|---|---|---|

Constant | 0.6449 (1.1291) | —— | —— | —— |

Log(P) | –0.0693 (–1.5858) | –0.2616 (–1.3914) | –0.0731* (–1.6609) | 0.2101 (0.7963) |

Log(UR) | –0.2600 (–1.2142) | 0.1818 (0.8779) | –0.1983 (–0.8826) | 0.4890 ** (2.2189) |

Log(GDPPC) | –0.1008 (–1.0772) | –0.0910 (–0.8083) | –0.1417 (–1.3574) | 0.1905 (1.0571) |

Log(EI) | 1.1225 *** (15.1500) | 1.1579 *** (4.6128) | 1.1284 *** (15.1838) | 1.1928 *** (4.2843) |

Log(IS) | 0.1321 (1.0852) | 0.2492 ** (2.2930) | 0.1208 (0.9918) | 0.1563 (1.3847) |

Log(ECS) | 0.2847 *** (3.4521) | 0.2583 *** (2.6862) | 0.2901 ***3.5199) | 0.2952 *** (3.0436) |

Log(EP) | –0.0611 (–0.2379) | –0.1480 ** (–2.2421) | 0.3289 (0.6455) | 0.0590 (0.4786) |

Log(OPEN) | 0.0804 ** (2.4208) | 0.0556 *** (2.7728) | 0.0895 *** (2.6282) | 0.0554 *** (2.6780) |

σ^{2} | 0.0069 | 0.0004 | 0.0068 | 0.0003 |

R^{2} | 0.8942 | 0.8013 | 0.8932 | 0.3492 |

Adjusted R^{2} | 0.8882 | 0.7915 | 0.8879 | 0.3171 |

Durbin–Watson | 2.0312 | 1.8704 | 2.0661 | 1.9776 |

Log-Likelihood | 164.6880 | 382.5538 | 165.5679 | 390.1138 |

LM Spatial Lag | 12.3664 (0.000) | 4.8069 (0.028) | 11.8219 (0.001) | 10.6239 (0.001) |

Robust LM Spatial Lag | 12.6202 (0.000) | 1.4754 (0.224) | 11.2804 (0.001) | 1.1991 (0.274) |

LM Spatial Error | 1.1145 (0.291) | 3.4387 (0.064) | 1.3975 (0.237) | 9.5364 (0.002) |

Robust LM Spatial Error | 1.3684 (0.242) | 0.1072 (0.743) | 0.8561 (0.355) | 0.1116 (0.738) |

Joint Test of Significance (LR) | Fixed-Effects | Statistics | df | p-Value |

Individual-Fixed Effects | 449.0918 | 30 | 0.0000 | |

Time-Fixed Effects | 15.1200 | 5 | 0.0099 |

## 5. Spillover Effects of Regional Carbon Emissions

_{it}= (I − δW)

^{−1}(X

_{it}β + WX

_{it}θ) + (I − δW)

^{−1}μ

_{i}+ (I − δW)

^{−1}λ

_{t}+ (I − δW)

^{−1}ε

_{it}

_{it}is the dependent variable of region i at time t, X

_{it}is a vector of independent variables of region i at time t, α is the constant term, θ is similar to β, which is a K × 1 vector of coefficients, μ

_{i}is individual-fixed effect, λ

_{t}is time-fixed effect. Taking partial derivatives of the kth independent variable X in both sides results in the following:

_{ij}is the (i, j) element of the matrix W. The direct effect is defined as the sum of the diagonal elements in the right matrix while the indirect effect is defined as the average of all the elements other than the diagonal elements (Lesage and Pace, 2009). Calculation of the direct and indirect effects by this method has a drawback because calculation (I − δW)

^{−1}is time consuming. To solve this, Lesage and Pace (2009) propose another method as follows:

^{−1}= I + δW + δ

^{2}W

^{2}+ δ

^{3}W

^{3}+ …

**Table 3.**Direct, indirect and total effect of the spatial Durbin model (Method 1, dependent variable: Log(CS)).

Variable | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|

Log(P) | 0.8695 *** (17.5550) | 0.2562 *** (3.5963) | 1.1257 *** (16.9993) |

Log(UR) | –0.6102 *** (–2.6245) | 1.4454 *** (4.8198) | 0.8352 *** (4.0497) |

Log(GDPPC) | 1.1107 *** (9.2912) | –0.6206 *** (–3.9366) | 0.4901 *** (4.3048) |

Log(EI) | 1.3005 *** (10.6820) | –0.4729 *** (–2.6714) | 0.8276 *** (6.1398) |

Log(IS) | 0.3498 *** (3.8936) | 0.7305 *** (5.2446) | 1.0804 *** (7.0120) |

Log(ECS) | 0.1102 (1.4101) | –0.2659 ** (–2.2764) | –0.1558 (–1.1617) |

Log(EP) | –0.0464 (–0.3767) | 0.0799 (0.3984) | 0.0335 (0.1862) |

Log(OPEN) | 0.0004 (0.0206) | –0.0528 ** (–2.2098) | –0.0524 (–1.7280) |

**Table 4.**Direct, indirect and total effect of the spatial Durbin model (Method 2, dependent variable: Log(CS)).

Variable | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|

Log(P) | 0.8680 *** (16.9634) | 0.2573 *** (3.7798) | 1.1253 *** (16.8913) |

Log(UR) | –0.5950 ** (–2.5072) | 1.4454 *** (4.7180) | 0.8404 *** (4.0538) |

Log(GDPPC) | 1.1075 *** (9.0225) | –0.6203 *** (–3.6545) | 0.4872 *** (4.2317) |

Log(EI) | 1.3057 *** (10.4525) | –0.4845 ** (–2.6801) | 0.8212 *** (5.9779) |

Log(IS) | 0.3525 *** (3.8938) | 0.7284 *** (5.3623) | 1.0809 *** (6.9873) |

Log(ECS) | 0.1053 (1.3456) | –0.2625 ** (–2.2113) | –0.1572 (–1.1692) |

Log(EP) | –0.0451 (–0.3586) | 0.0834 (0.4056) | 0.0383 (0.2008) |

Log(OPEN) | 0.0003 (0.0139) | –0.0536 ** (–2.3221) | –0.0534 * (–1.7624) |

**Table 5.**Direct, indirect and total effect of the spatial Durbin model (Method 1, dependent variable: Log(CI)).

Variable | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|

Log(P) | –0.1333 ** (–2.5375) | 0.2570 *** (3.8721) | 0.1237 * (1.9022) |

Log(UR) | –0.6072 *** (–2.6125) | 1.4402 *** (4.8072) | 0.8331 *** (4.1424) |

Log(GDPPC) | 0.1172 (0.9877) | –0.6246 *** (–3.7793) | –0.5075 *** (–4.4427) |

Log(EI) | 1.3100 *** (10.9563) | –0.4772 *** (–2.7687) | 0.8328 *** (6.1855) |

Log(IS) | 0.3433 *** (3.9225) | 0.7147 *** (5.5172) | 1.0580 *** (7.4353) |

Log(ECS) | 0.1115 ** (1.4542) | –0.2536 ** (–2.3062) | –0.1421 (–1.1075) |

Log(EP) | –0.0452 (–0.3634) | 0.0797 (0.4023) | 0.0345 (0.1890) |

Log(OPEN) | 0.0010 (0.0524) | –0.0506 ** (–2.2070) | –0.0496 (–1.6861) |

## 6. The Spatial Durbin Panel Data Model (SDPDM) for Carbon Emission

^{2}changed slightly after bias correction, while the coefficients for the spatial lagged dependent and independent variables are sensitive to it. Thus, the bias correction is necessary for the spatial Durbin model with both individual and time-fixed effects. The SDPDM has two hypotheses: H

_{0}: θ = 0 and H

_{0}: θ + δβ = 0. Rejection of both hypotheses indicates that SDPDM fits the data best. Both the Wald and LR tests reject the two hypotheses, thereby suggesting that both SLPDM and SEPDM are rejected. We thus opt for SDPDM. Meanwhile, the Houseman test points to (Model 5), of which the coefficients are in line with expectation, and the square correlation coefficient is greater than that of (Model 4).

**Table 6.**Direct, indirect and total effect of the spatial Durbin model (Method 2, dependent variable: Log(CI)).

Variable | Direct Effect | Indirect Effect | Total Effect |
---|---|---|---|

Log(P) | –0.1331 ** (–2.4595) | 0.2598 *** (3.9424) | 0.1267 ** (1.9568) |

Log(UR) | –0.6056 ** (–2.5655) | 1.4450 *** (4.6130) | 0.8394 *** (4.0677) |

Log(GDPPC) | 0.1181 (0.9869) | –0.6269 *** (–3.8520) | –0.5089 *** (–4.5164) |

Log(EI) | 1.3078 *** (10.7349) | –0.4754 *** (–2.8265) | 0.8324 *** (6.2488) |

Log(IS) | 0.3486 *** (3.8477) | 0.7194 *** (5.4372) | 1.0680 *** (6.8492) |

Log(ECS) | 0.1116 (1.4192) | –0.2601 ** (–2.2649) | –0.1485 (–1.1442) |

Log(EP) | –0.0507 (–0.3953) | 0.0924 (0.4544) | 0.0418 (0.2324) |

Log(OPEN) | 0.0008 (0.0423) | –0.0525 ** (–2.2719) | –0.0517 * (–1.7145) |

_{2}emissions. In carbon intensity estimation, P impacts the carbon intensity negatively and significantly (line 3, column 4 in Table 8), indicating that population growth did not enlarge the carbon intensity. Saving energy and reducing emissions have become important aspects in adjusting the structure of China’s economy and the path to development. Thus we have seen a declining trend in carbon intensity. For instance, in the 2009 United Nations Climate Change Conference, held in Copenhagen, Denmark, the Chinese government committed to reduce 40 to 50 percent of its CO

_{2}emissions by 2020 compared to 2005. Considering the fact that China is still undergoing urbanization and industrialization, as well as a surge of population and consumption per capita, a decrease of the carbon intensity has become the priority. China should follow the principle of “shared but differentiated responsibility” to develop its low-carbon economy in a sustainable way. The spatial lagged variable W × Log(P) impacts both the scale and intensity of carbon emissions significantly and positively, proving that there are carbon emissions spillover effects of population size. One should also note that the spillover effect of population is stronger at the scale of carbon emissions than carbon intensity. The possible reason is that spillover effects are generated by flow of population, such as interregional and rural–urban migration, and by a surge of CO

_{2}emissions from infrastructure development and household consumption.

**Table 7.**The spatial Durbin model with both individual and time-fixed effect (dependent variable: Log(CS)).

Variable | Spatial and Time-fixed Effect | Both Time and Individual Effects (Bias Corrected) | Spatial Random Effect and Time-Fixed Effect |
---|---|---|---|

W × Log(CS) | –0.2290 *** (–2.7392) | –0.1978 ** (–2.2988) | –0.2020 ** (–2.4198) |

Log(P) | 1.1820 *** (3.9562) | 1.1880 *** (3.4908) | 0.8871 *** (17.6901) |

Log(UR) | –0.2978 (–1.1976) | –0.3110 (–1.1002) | –0.4915 ** (–2.2262) |

Log(GDPPC) | 1.3010 *** (7.6561) | 1.3101 *** (6.7802) | 1.0642 *** (9.5461) |

Log(EI) | 0.8258 *** (3.4713) | 0.8429 *** (3.1212) | 1.2663 *** (11.2678) |

Log(IS) | 0.2827 *** (2.6770) | 0.2755 ** (2.2968) | 0.4039 *** (4.4186) |

Log(ECS) | 0.1940 ** (2.2499) | 0.1964 ** (1.9999) | 0.0900 (1.1606) |

Log(EP) | –0.0565 (–0.5608) | –0.0548 (–0.4775) | –0.0348 (–0.2952) |

Log(OPEN) | 0.0108 (0.6195) | 0.0113 (0.5677) | –0.0030 (–0.1595) |

W × Log(P) | 0.6424 (1.4861) | 0.6118 (1.2460) | 0.4634 *** (4.2059) |

W × Log(UR) | 1.8219 *** (5.1834) | 1.8099 *** (4.5221) | 1.4938 *** (4.8326) |

W × Log(GDPPC) | –0.0443 (–0.1559) | –0.0787 (–0.2454) | –0.4749 ** (–2.5301) |

W × Log(EI) | –1.2913*** (–3.1092) | –1.3385 *** (–2.8564) | –0.2720 (–1.3163) |

W × Log(IS) | 0.7874 *** (4.4070) | 0.7861 *** (3.8624) | 0.8859 *** (6.0133) |

W × Log(ECS) | –0.2082 (–1.4987) | –0.2113 (–1.3356) | –0.2700 ** (–2.0940) |

W × Log(EP) | –0.0378 (–0.2028) | –0.0360 (–0.1695) | 0.0828 (0.3832) |

W × Log(OPEN) | –0.0319 (–1.2736) | –0.0325 (–1.1394) | –0.0588 ** (–2.2985) |

teta | —— | —— | 0.1256 *** (5.5030) |

σ^{2} | 0.0002 | 0.0003 | 0.0003 |

R^{2} | 0.9982 | 0.9982 | 0.9974 |

Square Correlation Coefficient | 0.6573 | 0.6566 | 0.9615 |

Log-Likelihood | 422.9826 | 422.9826 | 333.4740 |

Wald Test Spatial Lag | 68.7413 (0.000) | 52.9481 (0.000) | 71.8871 (0.000) |

LR Test Spatial Lag | 56.5236 (0.000) | 56.5236 (0.000) | NA |

Wald Test Spatial Error | 65.5395 (0.000) | 51.4416 (0.000) | 63.8144 (0.000) |

LR Test Spatial Error | 52.4984 (0.000) | 52.4984 (0.000) | NA |

Hausman test | Statistics | df | p-Value |

11.1632 | 17 | 0.8480 |

**Table 8.**Spatial Durbin model with both individual and time-fixed effect (dependent variable: Log(CI)).

Variable | Spatial and Time-Fixed Effect | Both Time and Individual Effects (Bias Corrected) | Spatial Random Effect and Time-Fixed Effect |
---|---|---|---|

W × Log(CI) | –0.2290 *** (–2.7499) | –0.1980 ** (–2.3095) | –0.2220 *** (–2.6778) |

Log(P) | 0.1820 (0.6092) | 0.1879 (0.5522) | –0.1105 ** (–2.2183) |

Log(UR) | –0.2978 (–1.1977) | –0.3109 (–1.0999) | –0.4819 ** (–2.1899) |

Log(GDPPC) | 0.3010 * (1.7715) | 0.3100 * (1.6045) | 0.0593 (0.5332) |

Log(EI) | 0.8258 *** (3.4718) | 0.8428 *** (3.1211) | 1.2646 *** (11.3236) |

Log(IS) | 0.2827 *** (2.6774) | 0.2756 ** (2.2975) | 0.4093 *** (4.4913) |

Log(ECS) | 0.1940 ** (2.2500) | 0.1964 ** (1.9997) | 0.0890 (1.1512) |

Log(EP) | –0.0565 (–0.5608) | –0.0549 (–0.4777) | –0.0393 (–0.3332) |

Log(OPEN) | 0.0108 (0.6195) | 0.0113 (0.5675) | –0.0035 (–0.1831) |

W × Log(P) | 0.4135 (0.9741) | 0.4141 (0.8563) | 0.2617 *** (3.6790) |

W × Log(UR) | 1.8219 *** (5.1836) | 1.8100 *** (4.5226) | 1.4948 *** (4.8508) |

W × Log(GDPPC) | –0.2733 (–1.0161) | –0.2764 (–0.9023) | –0.6770 *** (–4.0574) |

W × Log(EI) | –1.2913 *** (–3.1103) | –1.3382 *** (–2.8563) | –0.2474 (–1.2043) |

W × Log(IS) | 0.7874 *** (4.4070) | 0.7862 *** (3.8625) | 0.8877 *** (6.0435) |

W × Log(ECS) | –0.2082 (–1.4987) | –0.2113 (–1.3354) | –0.2651 ** (–2.0601) |

W × Log(EP) | –0.0378 (–0.2028) | –0.0361 (–0.1696) | 0.0849 (0.3932) |

W × Log(OPEN) | –0.0319 (–1.2736) | –0.0325 (–1.1393) | –0.0577 ** (–2.2615) |

teta | —— | —— | 0.1266 *** (5.5034) |

σ^{2} | 0.0002 | 0.0003 | 0.0003 |

R^{2} | 0.9967 | 0.9967 | 0.9951 |

Square Correlation Coefficient | 0.5642 | 0.5634 | 0.9282 |

Log-Likelihood | 422.9334 | 422.9334 | 333.6113 |

Wald Test Spatial Lag | 66.1930 (0.000) | 51.3633 (0.000) | 65.0033 (0.000) |

LR Test Spatial Lag | 54.5750 (0.000) | 54.5750 (0.000) | NA |

Wald Test Spatial Error | 65.5435 (0.000) | 51.4385 (0.000) | 63.7257 (0.000) |

LR Test Spatial Error | 52.3998 (0.00) | 52.3998 (0.000) | NA |

Hausman Test | Statistics | df | P-Value |

0.9612 | 17 | 1.0000 |

_{2}emissions growth from economic growth and energy consumption. The positive and significant impact of per capita GDP on the scale and intensity of carbon emissions indicates that China’s unprecedented economic growth is the main driver of carbon emissions growth. Rising carbon emissions are to some extent unavoidable in maintaining a high growth rate. Thus, the carbon emissions reduction target is not likely to be met by controlling economic development, but by adjusting the structure of energy consumption and following the low-carbon path. We argue that economic growth and carbon emissions reduction can both be achieved. The negative and significant coefficient of the spatial lagged variable W × Log(GDPPC) shows that the economic growth in some regions can affect carbon emissions of other regions through spillover effects. The results also indicate that the spillover effect of economic development on the carbon intensity is greater than on the scale of carbon emission. This is explained by the fact that the provinces are competing to develop lower carbon economies in the 11

^{th}Five-Year period. The provinces have attempted to save energy and reduce emissions by adjusting the structure of economic growth and shifting towards low-carbon development paths. As a result, the effect of economic growth on carbon emissions has been alleviated.

## 7. Conclusions and Policy Recommendations

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Huang, Y.; Wang, L.; Li, G.; Jiang, D. An empirical analysis for the environmental Kuznets curve in China based on spatial penal models . South China J. Econ.
**2009**, 10, 59–68. (in Chinese). [Google Scholar] - Su, Z.; Hu, R.; Lin, S. Spatial econometric analysis of Kuznets’ relationship between environmental quality and economic growth. Geogr. Res.
**2009**, 2, 303–310. (in Chinese). [Google Scholar] - Zhu, P.; Yuan, J.; Zen, W. Analysis of Chinese Industry Environmental Kuznets Curve—Empirical Study Based on Spatial Panel Model. China Ind. Econ.
**2010**, 6, 65–74. (in Chinese). [Google Scholar] - Wang, L.; Guan, J.; Zhang, J. Environmental Pollution and Economic Growth in China: A Dynamic Spatial Panel Data Model. Geogr. Res.
**2010**, 6, 818–825. (in Chinese). [Google Scholar] - Yao, Y.; Ni, Q. The impact of foreign direct investment on carbon intensity empirical study based on Chinese provincial dynamic panel data. Econ. Geogr.
**2011**, 9, 1432–1438. (in Chinese). [Google Scholar] - Yu, Y. Innovation Cluster, Government Support and the Technological Innovation Efficiency: Based on Spatial Econometrics of Panel Data with Provincial Data. Econ. Rev.
**2011**, 2, 93–101. (in Chinese). [Google Scholar] - Zheng, Z.; Huang, H. Spatial panel statistical analysis on local government behavior and environmental pollution. Stat. Inf. Forum
**2011**, 10, 52–57. (in Chinese). [Google Scholar] - Chen, D.; Zhang, J. An empirical study on the environmental Kuznets curve for China’s carbon emission: Based on Spatial Panel model. Stat. Inf. Forum
**2011**, 5, 48–53. (in Chinese). [Google Scholar] - Xu, H.; Deng, Y. Does FDI lead to environment pollution in China? A spatial econometric analysis based on provincial panel data. Manag. World
**2012**, 2, 30–43. (in Chinese). [Google Scholar] - Wang, H.; Teng, Y. Economic Development and Environmental Pollution Space Panel Data Analysis. Technol. Econ. Manag. Res.
**2013**, 2, 85–89. (in Chinese). [Google Scholar] - Ehrlich, P.R.; Holdren, J.P. Impact of population growth. Science
**1971**, 3977, 1212–1217. [Google Scholar] - Harrison, P. Inside the Third World: The Anatomy of Poverty; Penguin Books: New York, NY, USA, 1981. [Google Scholar]
- Raskin, P.D. Methods for estimating the population contribution to environmental change. Ecol. Econ.
**1995**, 3, 225–233. [Google Scholar] - York, R.; Rosa, E.A.; Dietz, T. Bridging environmental science with environmental policy: Plasticity of population, affluence, and technology. Soc. Sci. Q.
**2002**, 1, 18–34. [Google Scholar] - Shi, A. The impact of population pressure on global carbon dioxide emissions, 1975–1996: Evidence from pooled cross-country data. Ecol. Econ.
**2003**, 1, 29–42. [Google Scholar] [CrossRef] - Cole, M.A.; Neumayer, E. Examining the impact of demographic factors on air pollution. Popul. Environ.
**2004**, 1, 5–21. [Google Scholar] [CrossRef] - Rosa, E.A.; York, R.; Dietz, T. Tracking the anthropogenic drivers of ecological impacts. AMBIO A. J. Hum. Environ.
**2004**, 8, 509–512. [Google Scholar] - Waggoner, P.E.; Ausubel, J.H. A framework for sustainability science: A renovated IPAT identity. Proc. Nat. Acad. Sci. USA
**2002**, 12, 7860–7865. [Google Scholar] [CrossRef] - Dietz, T.; Rosa, E.A. Rethinking the environmental impacts of population, affluence and technology. Hum. Ecol. Rev.
**1994**, 1, 277–300. [Google Scholar] - Dietz, T.; Rosa, E.A. Effects of population and affluence on CO
_{2}emissions. Proc. Nat. Acad. Sci. USA**1997**, 1, 175–179. [Google Scholar] [CrossRef] - York, R.; Rosa, E.A.; Dietz, T. STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecol. Econ.
**2003**, 3, 351–365. [Google Scholar] [CrossRef] - National Bureau of Statistics of People’s Republic of China. China Energy Statistical Yearbook 2011; China Statistics Press: Beijing, China, 2012.
- LeSage, J.P.; Pace, R.K. An Introduction to Spatial Econometrics; CRC Press: Boca Raton, FL, USA, 2009; pp. 19–44. [Google Scholar]
- Lee, L.; Yu, J. Estimation of spatial autoregressive panel data models with fixed effects. J. Econ.
**2010**, 2, 165–185. [Google Scholar] [CrossRef]

© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Liu, Y.; Xiao, H.; Zikhali, P.; Lv, Y.
Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level. *Sustainability* **2014**, *6*, 6005-6023.
https://doi.org/10.3390/su6096005

**AMA Style**

Liu Y, Xiao H, Zikhali P, Lv Y.
Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level. *Sustainability*. 2014; 6(9):6005-6023.
https://doi.org/10.3390/su6096005

**Chicago/Turabian Style**

Liu, Yu, Hongwei Xiao, Precious Zikhali, and Yingkang Lv.
2014. "Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level" *Sustainability* 6, no. 9: 6005-6023.
https://doi.org/10.3390/su6096005