本文重新檢視經濟成長與環境品質之間的長期關係，並以 PM2.5 年平均暴露濃度作為環境指標。傳統環境顧志耐曲線（Environmental Kuznets Curve, EKC）通常以跨國資料估計一條共同的所得多項式，檢驗全球各國是否共同遵循污染隨所得提高而先上升、後下降的倒 U 型關係。然而，這種作法涉及兩個問題。第一，污染與所得可能具有非平穩的時間序列特性，各國又同時受到全球景氣、能源價格與技術變遷等共同衝擊，使傳統估計可能將共同趨勢誤認為穩定的污染－所得關係。第二，一條共同曲線預設所有國家遵循相同的經濟成長與環境之間的關係，但各國的產業結構、發展程度與治理條件不同，未必具有相同的環境轉型路徑。因此，本文不再以尋找共同 EKC 為出發點，而是探討 PM2.5 與所得是否存在長期關係、此一關係能否由共同係數概括，以及國家間差異可能來自哪些條件。
實證結果顯示，PM2.5 與所得整體上存在長期共整合關係，污染偏離長期關係後亦具有向均衡調整的力量。Hausman 型檢定拒絕共同長期所得係數的限制，支持各國 PM2.5 －所得長期關係具有異質性。在 173 國中，102 國的長期所得係數為負、71 國為正，顯示各國關係方向並不一致。此外，不同所得組別的係數分布明顯重疊，說明所得水準無法單獨決定一國的污染－所得關係。
進一步追問這些差異從何而來，第二階段結果顯示，平均所得較高的國家，其長期所得係數整體上較低；但在控制所得、政府效能與降雨量後，二級產業相對於三級產業的比重越高，長期所得係數也越高，而且此一結果在擴充設定中仍維持正向且顯著。換言之，當經濟成長較依賴工業部門時，所得增加較可能伴隨PM2.5 上升，或較不容易轉化為空氣品質改善。
上述結果建立於 1990 至 2020 年、173 國的平衡追蹤資料。實證分析先檢驗橫斷面相依性、整合階數與面板共整合關係，再採用考量橫斷面相依性的誤差修正模型，即共同相關效果誤差修正模型（Common Correlated Effects Error Correction Model, CCE-ECM），控制不可觀察的跨國共同因素。本文接著比較允許各國長期係數不同的 CCE-MG 模型與限制共同長期係數的 CCE-PMG 模型；確認國家差異後，再以加權最小平方法分析其異質性來源。
綜合而言，所得反映一國發展到何種程度，產業結構則反映一國透過何種方式發展。環境改善不是所得提高後必然發生的結果；EKC 較適合被理解為特定發展條件下可能形成的長期轉型，而非所有國家都會自動遵循的普遍法則。理解經濟成長與環境之間的關係，關鍵不只在一國有多富裕，更在於一國如何變得富裕。

This thesis examines whether economic growth has a common long-run relationship with environmental pollution across countries. Conventional studies of the Environmental Kuznets Curve (EKC) often pool countries to estimate a common inverted-U pollution–income relationship. This approach raises two concerns. Because pollution and income may be nonstationary and exposed to common global shocks, estimated associations may reflect shared trends rather than stable long-run relations. Imposing a single curve also assumes that all countries follow the same environmental transition despite differences in industrial structure, development, and governance. This study asks whether PM2.5 ex-posure and income are linked in the long run, whether that relationship differs across countries, and what is associated with those differences.
The results support cointegration between PM2.5 exposure and income, together with adjustment toward long-run equilibrium. The Hausman-type test rejects the common-coefficient restriction, supporting heterogeneous long-run relationships. Country estimates also vary in sign: 102 are negative and 71 are positive. Their distributions overlap considerably across income groups, indicating that income alone does not determine whether further growth is associated with higher or lower PM2.5 exposure.
The second-stage results show that higher average income is associated with lower long-run income coefficients. After controlling for income, government effectiveness, and precipitation, a higher ratio of secondary to tertiary industry is significantly associated with a higher coefficient; this result remains robust across extended specifications. This pattern suggests that growth in more industry-dependent economies is less likely to be accompanied by improvements in air quality.
These findings are based on a balanced panel of 173 countries over 1990–2020. The empirical analysis first tests for cross-sectional dependence, integration, and panel cointe-gration, and then estimates a Common Correlated Effects Error Correction Model (CCE-ECM). The model uses cross-sectional averages to account for unobserved shared shocks and distinguishes short-run dynamics from long-run adjustment. A Hausman-type test compares the heterogeneous CCE-MG specification with the common-coefficient CCE-PMG specification. A second-stage weighted least squares regression then relates the country-specific coefficients to national characteristics.
Rising income does not automatically improve environmental quality. Income captures the level of development, whereas industrial structure reflects the development path. The EKC is better understood as a transition that may arise under particular conditions than as a universal path followed by all countries.

Antweiler, Werner, Brian R. Copeland, and M. Scott Taylor (2001). “Is Free Trade Good for the Environment?” In: American Economic Review 91.4, pp. 877–908.
Awe, Yewande Aramide et al. (2020). The Global Health Cost of Ambient PM2.5 Air Pollution. Report AUS0001948. Washington, DC: World Bank.
Bruyn, Sander M. de (1997). “Explaining the Environmental Kuznets Curve: Structural Change and International Agreements in Reducing Sulphur Emissions”. In: Environment and Development Economics 2.4, pp. 485–503.
Carson, Richard T. (2010). “The Environmental Kuznets Curve: Seeking Empirical Regularity and Theoretical Structure”. In: Review of Environmental Economics and Policy 4.1, pp. 3–23.
Chudik, Alexander and M. Hashem Pesaran (2015). “Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors”. In: Journal of Econometrics 188.2, pp. 393–420.
Copeland, Brian R. and M. Scott Taylor (2004). “Trade, Growth, and the Environment”. In: Journal of Economic Literature 42.1, pp. 7–71.
Dasgupta, Susmita et al. (2002). “Confronting the Environmental Kuznets Curve”. In: Journal of Economic Perspectives 16.1, pp. 147–168.
Dijkgraaf, Elbert and Herman R. J. Vollebergh (2005). “A Test for Parameter Homogeneity in CO2 Panel EKC Estimations”. In: Environmental and Resource Economics 32.2, pp. 229–239.
Ditzen, Jan (2018). “Estimating Dynamic Common-Correlated Effects in Stata”. In: The Stata Journal 18.3, pp. 585–617.
Engle, Robert F. and Clive W. J. Granger (1987). “Co-Integration and Error Correction: Representation, Estimation, and Testing”. In: Econometrica 55.2, pp. 251–276.
Farzin, Y. Hossein and Craig A. Bond (2006). “Democracy and Environmental Quality”. In: Journal of Development Economics 81.1, pp. 213–235.
Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer (2015). “The Next Generation of the Penn World Table”. In: American Economic Review 105.10, pp. 3150–3182.
Figueroa, Eugenio and Roberto Pastén (2009). “Country-Specific Environmental Kuznets Curves: A Random Coefficient Approach Applied to High-Income Countries”. In: Estudios de Economía 36.1, pp. 5–32.
Fredriksson, Per G. et al. (2005). “Environmentalism, Democracy, and Pollution Control”. In: Journal of Environmental Economics and Management 49.2, pp. 343–365.
Grossman, Gene M. and Alan B. Krueger (1991). Environmental Impacts of a North American Free Trade Agreement. NBER Working Paper 3914. National Bureau of Economic Research.
— (1995). “Economic Growth and the Environment”. In: The Quarterly Journal of Economics 110.2, pp. 353–377.
Harbaugh, William T., Arik Levinson, and David Molloy Wilson (2002). “Reexamining the Empirical Evidence for an Environmental Kuznets Curve”. In: The Review of Economics and Statistics 84.3, pp. 541–551.
He, Jie and Hua Wang (2012). “Economic Structure, Development Policy and Environmental Quality: An Empirical Analysis of Environmental Kuznets Curves with Chinese Municipal Data”. In: Ecological Economics 76, pp. 49–59.
Hettige, Hemamala, Muthukumara Mani, and David Wheeler (2000). “Industrial Pollution in Economic Development: The Environmental Kuznets Curve Revisited”. In: Journal of Development Economics 62.2, pp. 445–476.
Jobert, Thomas, Fatih Karanfil, and Anna Tykhonenko (2014). “Estimating Country-Specific Environmental Kuznets Curves from Panel Data: A Bayesian Shrinkage Approach”. In: Applied Economics 46.13, pp. 1449–1464.
Kao, Chihwa (1999). “Spurious Regression and Residual-Based Tests for Cointegration in Panel Data”. In: Journal of Econometrics 90.1, pp. 1–44.
List, John A. and Craig A. Gallet (1999). “The Environmental Kuznets Curve: Does One Size Fit All?” In: Ecological Economics 31.3, pp. 409–423.
Millimet, Daniel L., John A. List, and Thanasis Stengos (2003). “The Environmental Kuznets Curve: Real Progress or Misspecified Models?” In: The Review of Economics and Statistics 85.4, pp. 1038–1047.
Pedroni, Peter (1999). “Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors”. In: Oxford Bulletin of Economics and Statistics 61.S1, pp. 653–670.
— (2004). “Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP Hypothesis”. In: Econometric Theory 20.3, pp. 597–625.
Perman, Roger and David I. Stern (2003). “Evidence from Panel Unit Root and Cointegration Tests that the Environmental Kuznets Curve Does Not Exist”. In: Australian Journal of Agricultural and Resource Economics 47.3, pp. 325–347.
Pesaran, M. Hashem (2004). General Diagnostic Tests for Cross Section Dependence in Panels. Cambridge Working Papers in Economics 0435. University of Cambridge.
— (2006). “Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure”. In: Econometrica 74.4, pp. 967–1012.
Pesaran, M. Hashem (2007). “A Simple Panel Unit Root Test in the Presence of Cross-Section Dependence”. In: Journal of Applied Econometrics 22.2, pp. 265–312.
Pesaran, M. Hashem, Yongcheol Shin, and Ron P. Smith (1999). “Pooled Mean Group Estimation of Dynamic Heterogeneous Panels”. In: Journal of the American Statistical Association 94.446, pp. 621–634.
Pope, C. Arden and Douglas W. Dockery (2006). “Health Effects of Fine Particulate Air Pollution: Lines that Connect”. In: Journal of the Air & Waste Management Association 56.6, pp. 709–742.
Torras, Mariano and James K. Boyce (1998). “Income, Inequality, and Pollution: A Reassessment of the Environmental Kuznets Curve”. In: Ecological Economics 25.2, pp. 147–160.
Wagner, Martin (2015). “The Environmental Kuznets Curve, Cointegration and Nonlinearity”. In: Journal of Applied Econometrics 30.6, pp. 948–967.