Economic Research and Perspectives

Economic Research and Perspectives

The Indirect Effects of Globalization on Sustainable Resource Management: A PSTR Approach

Document Type : Original Article

Authors
Afsaneh Mafi Asl 1
Hassan Khodavaisi 2
Yusef Mohammadzadeh 2
1 . Ph.D. Student of International Economics, Department of Economics, Faculty of Economics and management, Urmia University, Urmia, Iran
2 Associate Professor, Department of Economics, Faculty of Economics and management, Urmia University, Urmia, Iran
10.48311/ecor.2025.24049
Abstract
The increasing use of natural resources and materials raises significant concerns, including economic issues—such as the rising costs of resource and materials management, inefficient usage, and unsustainable development—as well as environmental effects like carbon dioxide emissions, global warming, and climate change. Key factors such as resource productivity, economic growth, and business activities are closely related to the dynamics of domestic material consumption (DMC). Empirical research indicates that trade increasingly influences global resource use. Rising global demand for exports and imports fosters greater production of goods and services, which in turn places pressure on the use of natural resources and materials. Moreover, globalization expands the scale of economic activities such as trade and transportation, leading to increased resource use and pollution. While international organizations and policymakers have historically emphasized economic development and employment in discussions on human progress, recent attention has shifted toward inequality and environmental sustainability. This study addresses this shift, examining the significant role globalization plays in influencing environmental quality and sustainable resource use.

Aim and Introduction
The optimal and sustainable use of natural resources is critical for mitigating environmental risks and preventing resource depletion. While most studies on resource consumption assume a linear relationship between economic growth, population, and resource use, this research explores the often-overlooked nonlinear relationships between consumption and factors such as globalization, urbanization, and total factor productivity (TFP).

Globalization—encompassing social, political, and economic dimensions—plays a pivotal role in shaping production, consumption, and environmental outcomes. This study employs the Panel Smooth Transition Regression (PSTR) model to analyze how globalization influences the relationship between economic growth and domestic material consumption. The PSTR model, which accounts for regime shifts, enables the examination of nonlinear interactions and offers more precise empirical insights into these relationships. By allowing parameters to vary over time and across countries, the model captures the heterogeneous effects of economic development on resource consumption.
The primary objective is to investigate the nonlinear effects of globalization on gross domestic product (GDP) and resource consumption at different globalization stages. By leveraging the PSTR model, this study provides a nuanced understanding of the complex and dynamic relationships among economic growth, globalization, and environmental sustainability.

Methodology
This study hypothesizes that domestic material consumption responds differently to varying levels of globalization. Specifically, it posits that the effect of economic growth changes across different globalization stages, implying a nonlinear globalization threshold in the relationship between economic growth and DMC. The PSTR method is well-suited to capturing nonlinearities, whether they involve steep or gradual regime transitions. 

The corresponding PSTR model is specified as follows:

LDMCit=μi+δ0Xit+δ1Xit*GSit;γ;c+εit with γ>0 (1)[A1]

where I[A2] = 1, 2, ..., n and t = 1, 2, ..., t[A3] represent the number of cross-sections and time periods, respectively. LDMC denotes the logarithm of domestic material consumption. Xit= (X1t، X2t) includes per capita GDP and other control variables such as urbanization, total factor productivity growth, and industrial value-added. GSit;γ;c is the smooth transition function, where Sit denotes the globalization index.

A simplified version of the model is:

LDMCit=μi+δ0LGDPit+δ1LGDPit*GSit;γ;c+εit γ>0 (2)

Following Gonzales et al. (2005), the transition function is assumed to be logistic:

GSit;γ;c= 1+exp-γj=1nSit-cj-1 (3)

here, γ is the slope parameter, and cj , j=1, 2, …, n is an n-dimensional location vector representing the threshold values. Depending on the degree of globalization, the effect of economic growth on DMC changes. In the first regime (where G.=0 ), the elasticity of DMC with respect to economic growth is δ0 . In the second regime, this elasticity becomes δ0+δ1 . The estimation procedure for the PSTR model involves the following steps:
(i) The null hypothesis of linearity is tested against the alternative of a smooth transition. If linearity is rejected, the appropriate threshold variable Sit is selected. To address the issue of unidentified nuisance parameters under the null hypothesis (as discussed by Luukkonen et al., 1988), the transition GSit;γ;c is approximated around γ = 0 using a first-order Taylor expansion. The linearized form of the model is:

LDMCit=μi+δ0Xit+δ1XitLKOFit+…+δmXitLKOFitm+εit (4)

The linearity test between LDMC and LKOF is equivalent to testing the null hypothesis H0: δ0=δ1=δm in equation (4). This test can be performed using an LM test, which follows an F distribution:

LMF=SSR0-SSR1/mKSSR0/TN-N-mK~FmK,TN-N-K-mK+1 (5)

where SSR0 and SSR1 represent the panel sum of squared residuals under H0 and H1 , respectively. Here, K denotes the number of explanatory variables, T is the number of years, N is the number of countries, and m is the order of the logistic transfer function in equation (5).
(ii) The number r of transition functions and the number of extreme regimes, equal to r + 1, must be specified. For example, r = 1 indicates the presence of two regimes associated with the threshold variable in the PSTR model. When globalization is used as the threshold variable, the effect of economic growth on LDMC varies across different levels of globalization, which are defined according to the estimated threshold.
(iii) The parameters of the selected PSTR model are estimated using nonlinear least squares after data reduction, following the procedures outlined by Hansen (1999) and Gonzalez et al. (2005, 2017). 
(iv) The validity of the estimated PSTR model is assessed by testing for the absence of residual heterogeneity. Accordingly, this study examines the indirect effect of globalization on environmental impact assessment and sustainable resource management in selected countries worldwide.

Results and Discussion
The results indicate that the null hypothesis of linearity is rejected, confirming that the effects of economic growth, urbanization, industrialization, and total factor productivity (TFP) on domestic material consumption (DMC) vary with the level of globalization. The model features a threshold with two regimes and is estimated using a Panel Smooth Transition Regression (PSTR) approach, which is particularly well-suited for heterogeneous panel data. This study examines the indirect impact of globalization on sustainable resource management across 102 countries over the period 2000–2020. 
The estimated transition speed from the first regime to the second is 102.0487, with the regime change occurring at a threshold value of 4.3998. In the first regime, the logarithm of total factor productivity exerts a positive and statistically significant effect on the logarithm of domestic material consumption, indicating that at lower levels of globalization, productivity growth drives industrial expansion that relies heavily on domestic materials. This, in turn, contributes to increased resource use and poses challenges for sustainable resource management. 
However, as globalization intensifies and exceeds the identified threshold, the positive effect of TFP on DMC diminishes and eventually becomes negative. At higher levels of globalization, improvements in productivity are associated with the adoption of pollution control technologies and increased investment in cleaner production methods, leading to reduced material consumption and enhanced sustainability outcomes. These findings align with theoretical expectations and are consistent with empirical evidence from Ulucak et al. (2020) and Ma et al. (2023). 
Urbanization displays a similarly nonlinear pattern. At lower globalization levels, urbanization is associated with a reduction in DMC. However, beyond the threshold, urbanization leads to increased material consumption. This shift reflects the reliance of large segments of the population in developing countries on traditional energy sources, which intensifies pollution. Furthermore, large-scale rural-to-urban migration results in habitat destruction, deforestation, and heightened pressure on ecosystems. Inadequate urban planning and the lack of environmentally sustainable infrastructure exacerbate environmental degradation. These observations are in line with the theoretical framework and corroborated by Ulucak et al. (2020), Ma et al. (2023), and Ullah and Lin (2024). 
In contrast, industrialization consistently increases domestic material consumption across both regimes, thereby worsening sustainable resource management. This finding is also consistent with theoretical predictions and empirical studies by Ullah and Lin (2024).

Conclusion
Efficient and sustainable resource use is essential to mitigating environmental threats and preventing resource depletion. A robust approach to sustainable resource management involves identifying and analyzing the key drivers of material consumption. Following the STIRPAT framework, this study investigates the effects of economic growth, total factor productivity, industrialization, urbanization, and globalization on domestic material consumption. 
The empirical results reveal that TFP initially increases DMC in the first regime. However, after crossing the globalization threshold, TFP contributes to a decline in material consumption, thereby supporting sustainable resource management. Economic growth follows a similar pattern: while it increases DMC in the first regime, it reduces consumption in the second. Urbanization also has contrasting effects, reducing DMC at lower globalization levels but increasing it after the threshold is crossed. In contrast, industrialization has a consistently positive effect on DMC in both regimes, underscoring its detrimental impact on sustainable resource use.
These findings highlight the importance of tailoring resource management strategies to globalization dynamics and provide valuable insights for policymakers aiming to align economic development with environmental sustainability

[A1]In English, all numbers must be left aligned.
Pls revise all numbers and transfer them to the left side.
[A2]You meant i?
[A3]You meant T?











Keywords
Domestic Material Consumption
Globalization
PSTR
STIRPAT
Sustainable Resource Management

Subjects

Auci, S. & Vignani, D. (2013). Environmental Kuznets curve and domestic material consumption indicator: An European analysis. Munich Pers. RePEc Arch. Pap. 52882, 34.
Baninla, Y., Zhang, M., Lu, Y., Liang, R., Zhang, Q., Zhou, Y. & Khan, K. (2019). A transitional perspective of global and regional mineral material flows. Resources, Conservation and Recycling. 140, 91–101.
Baniya B., Giurco, D. & Kelly, S. (2021a). Green growth in Nepal and Bangladesh: empirical analysis and future prospects. Energy Policy, 149, 112049.
Bilgili, F., Koçak, E. & Bulut, Ü. (2016). The dynamic impact of renewable energy consumption on CO2emissions: a revisited Environmental Kuznets Curve approach. Renewable and Sustainable Energy Reviews, 54, 838-845.
Bloomberg (2019). How the Rise of Developing Countries Has Disrupted Global Trade. https://www.bloomberg.com/graphics/2019-bloomberg-new-economy/global-trade-developing-nations/.
Canas, A., Ferrao, P. & Conceicazo, P. (2003). A new environmental Kuznets curve? Relationship between direct material input and income per capita: evidence from industrialized countries. Ecological Economics. 46, 217–229.
Chen, X. & Liu, Zh. (2024). Fintech and sustainable resources management: Role of trade openness and globalization in BRICS countries. Resources Policy, 90, 104700.
Chiu, A.S.F., Dong, L., Dong, L., Geng, Y., Rapera, C., Tan, E., 2017. Philippine resource efficiency in Asian context: status, trends and driving forces of Philippine material flows from 1980 to 2008. J. Clean. Prod. 153, 63–73.
Cibulka, S., Giljum, S. (2020). Towards a comprehensive framework of the relationships between resource footprints, quality of life, and economic development. Sustainability, 12 (11). https://doi.org/10.3390/SU12114734.
Copeland, B.R. & Taylor, M.S. (2004). Trade, growth, and the environment. Journal of Economic Literature. 42 (1), 7–71.
Copeland, B.R. & Taylor, M.S. (2013). Trade and the Environment: Theory and Evidence. Princeton University Press, New Jersey.
Corona, P., Franceschi, S., Pisani, C., Portoghesi, L., Mattioli, W. & Fattorini, L. (2017). Inference on diversity from forest inventories: a review. Biodiversity and Conservation, 26, 3037-3049.
De Vos, I. & Westerlund, J. (2019). On CCE estimation of factor-augmented models when regressors are not linear in the factors. Economics Letters, 178, 5–7.
Dewulf, J., Hellweg, S., Pfister, S., Le´on, M.F.G., Sonderegger, T., de Matos, C.T., Blengini, G.A., Mathieux, F. (2021). Towards sustainable resource management: identification and quantification of human actions that compromise the accessibility of metal resources. Resour. Conserv. Recycl. 167 https://doi.org/10.1016/j. resconrec.2021.105403.
Díaz, S. & Cristina, P. (2012). Factors of material consumption. http://dspace.mit.edu/handle/1721.1/7582.
Dietz, T., A Rosa, E. & York, R. (2007). Driving the human ecological footprint. Frontiers in Ecology and the Environment, 5, 13–18.
Dinda, S. (2004). Environmental kuznets curve hypothesis: a survey. Ecological Economics, 49, 431-455.
Dong, L., Dai, M., Liang, H., Zhang, N., Mancheri, N., Ren, J., Dou, Y. & Hu, M. (2017). Material flows and resource productivity in China, South Korea and Japan from 1970 to 2008: a transitional perspective. Journal of Cleaner Production, 141, 1164–1177.
Dreher, A. (2006). Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 38, 1091–1110.
Dreher, A., Gaston, N. & Martens, P. (2008). Measuring Globalization - Gauging its Consequences. Springer, New York, NY.
Duran, H.E. (2019). Asymmetries in regional development: does TFP or capital accumulation matter for spatial inequalities? The Journal of Economic Asymmetries, 20, e00119. https://doi.org/10.1016/j.jeca.2019.e00119.
Duro, J.A., Schaffartzik, A.& Krausmann, F. (2018). Metabolic inequality and its impact on efficient contraction and convergence of international material resource use. Ecological Economics, 145,430–440.
Ehrlich, P.R. & Holdren, J.P. (1971). Impact of population growth. Science, 171 (80), 1212-1217.
George, G., Jan Schillebeeckx, S.D. & Health System, A. (2015). The management of natural resources: an overview and research agenda. Academy of Management Journal, 58, 1595–1613.
Gonzalez, A., Ter€asvirta, T. & Van Djik, D. (2005). Panel smooth transition regression models (No. 604), SSE/EFI working paper, series in economics and finance.
Gonzalez, A., Ter€asvirta, T., van Dijk, D. & Yang, Y. (2017). Panel smooth transition regression models. SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics.
Grabarczyk, P., Wagner, M., Frondel, M. & Sommer, S. (2018). A cointegrating polynomial regression analysis of the material kuznets curve hypothesis. Resour. Pol. 57, 236–245.
Grossman, G.M. & Krueger, A.B. (1991). Environmental impacts of a north American free trade agreement. NBER Working Papers 3914, National Bureau of Economic Research, Inc.
Grossman, G.M. & Krueger, A.B. (1995). Economic growth and the environment. The Quarterly Journal of Economics, 110, 353–377.
Guo, X., Meng, X., Luan, Q. & Wang, Y. (2023). Trade openness, globalization, and natural resources management: The moderating role of economic complexity in newly industrialized countries. Resources Policy, 85, 103757.
Gygli, S., Haelg, F., Potrafke, N. & Sturm, J.E. (2019). The KOF globalization index – revisited. The Review of International Organizations. 14, 543-574.
Hansen, B.E. (1999). Threshold effects in non-dynamic panels: estimation, testing, and inference. Journal of Econometrics. 93, 345–368.
Jaunky, V.C. (2012). Is there a material Kuznets curve for aluminium? Evidence from rich countries Resources Policy, 37, 296–307. https://doi.org/10.1016/j. resourpol.2012.04.001.
Ji, X., Chen, X., Mirza, N. & Umar, M. (2021a). Sustainable energy goals and investment premium: evidence from renewable and conventional equity mutual funds in the Euro zone. Resources Policy, 74, 102387 https://doi.org/10.1016/j. resourpol.2021.102387.
Karakaya, E., Sarı, E., & Alataş, S. (2021). What drives material use in the EU? Evidence from club convergence and decomposition analysis on domestic material consumption and material footprint. Resources Policy, 70, 101904.
Kassouri, Y., Alola, A.A. & Savas¸ S. (2020). The dynamics of material consumption in phases of the economic cycle for selected emerging countries. Resources Policy, 70, 101918.
Kirikkaleli, D., Güng¨or, H. & Adebayo, T.S. (2022). Consumption-based carbon emissions, renewable energy consumption, financial development and economic growth in Chile. Bus. Strat. Environ. 31 (3), 1123–1137.
Koçak, E. & Ulucak, Z.S. (2019). The effect of energy R&D expenditures on CO2 emission reduction: estimation of the STIRPAT model for OECD countries. Environmental Science and Pollution Research. 26 (14), 14328-14338.
Krausmann, F., Wiedenhofer, D., Lauk, C., Haas, W., Tanikawa, H., Fishman, T., Miatto, A., Schandl, H., Haberl, H. (2010). Global socioeconomic material stocks rise 23-fold over the 20th century and require half of annual resource use. Proceedings of the National Academy of Sciences, 114 (8), 1880-1885.
Lasisi, T.T., Eluwole, K.K., Alola, U.V., Aldieri, L., Vinci, C.P. & Alola, A.A. (2021). Do Tourism Activities and Urbanization Drive Material Consumption in the OECD Countries? A Quantile Regression Approach. Sustainability, MDPI, 13(14), 1-13.
Lenzen, M., Geschke, A., West, J., Fry, J., Malik, A., Giljum, S., Mil`a i Canals, L., Pi˜nero, P., Lutter, S., Wiedmann, T., Li, M., Sevenster, M., Potoˇcnik, J., Teixeira, I., Van Voore, M., Nansai, K. & Schandl, H., (2022). Implementing the material footprint to measure progress towards Sustainable Development Goals 8 and 12. Nature Sustainability, 5 (2), 157–166. https://doi.org/10.1038/s41893-021-00811-6.
Li, Q., Dai, T., Wang, G., Cheng, J., Zhong, W., Wen, B. & Liang, L. (2018). Iron material flow analysis for production, consumption, and trade in China from 2010 to 2015. Journal of Cleaner Production, 172, 1807–1813.
Luukkonen, R., Saikkonen, P. & Ter€Asvirta, T. (1988). Testing linearity against smooth transition autoregressive models. Biometrika, 75, 491–499.
Lv, Z. & Xu, T. (2018). Is economic globalization good or bad for the environmental quality? New evidence from dynamic heterogeneous panel models. Technological Forecasting and Social Change,137, 340–343.
M´arquez-Ramos, L. (2015). The relationship between trade and sustainable transport: a quantitative assessment with indicators of the importance of environmental performance and agglomeration externalities. Ecological Indicators. 52, 170–183.
M´arquez-Ramos, L. (2015). The relationship between trade and sustainable transport: a quantitative assessment with indicators of the importance of environmental performance and agglomeration externalities. Ecological Indicators, 52, 170–183.
Ma, Y., Fan, Y. & Razzaq, A. (2023). Influence of technical efficiency and globalization on sustainable resources management: Evidence from South Asian countries. Resources Policy, 81, 103281.
Martinico-Perez, M.F.G., Fishman, T., Okuoka, K. & Tanikawa, H. (2017). Material flow accounts and driving factors of economic growth in the Philippines. Journal of Industrial Ecology, 21, 1226–1236.
Mozner, Z.V. (2013). A consumption-based approach to carbon emission accounting–sectoral differences and environmental benefits. Journal of Cleaner Production, 42, 83–95.
Nassani, A.A., Awan, U., Zaman, K., Hyder, S., Aldakhil, A.M. & Abro, M.M.Q. (2019). Management of natural resources and material pricing: global evidence. Resources Policy, 64, 101500, https://doi.org/10.1016/j.resourpol.2019.101500.
O’Neill D.W., Fanning, A.L., Lam, W.F. & Steinberger, J.K. (2018). A good life for all within planetary boundaries. Nature Sustainability 1(2), 88–95.
Plank, B., Eisenmenger, N., Schaffartzik, A. & Wiedenhofer, D. (2018). International trade drives global resource use: a structural decomposition analysis of raw material consumption from 1990–2010. Environmental Science & Technology, 52 (7), 4190–4198.
Pothen, F. & Welsch, H. (2019). Economic development and material use. Evidence from international panel data. World Development. 115, 107–119. https://doi.org/10.1016/j. worlddev.2018.06.008.
Ramzan, M., Sheng, B., Shahbaz, M., Song, J. & Jiao, Z. (2019). Impact of trade openness on GDP growth: does TFP matter? The Journal of International Trade & Economic Development, 28, 960–995. https://doi. org/10.1080/09638199.2019.1616805.
Razzaq, A., Sharif, A., Afshan, S., & Li, C. J. (2023). Do climate technologies and recycling asymmetrically mitigate consumption-based carbon emissions in the United States? New insights from Quantile ARDL. Technol. Forecast. Soc. Change, 186, 122138.
Ren, S., Liu, Z., Zhanbayev, R. & Du, M., (2022). Does the internet development put pressure on energy-saving potential for environmental sustainability? Evidence from China. Journal of Economic Analysis, 1, 50–65. https://doi.org/10.58567/jea01010004.
Rudolph, A. & Figge, L. (2017). Determinants of Ecological Footprints: what is the role of globalization? Ecological Indicators, 81, 348–361. https://doi.org/10.1016/J. ECOLIND.2017.04.060.
Sarkodie, S.A. (2020). Causal effect of environmental factors, economic indicators and domestic material consumption using frequency domain causality test. Science of The Total Environment, 736, 139602.
Shah, I.H., Dong, L. & Park, H.S. (2019). Characterization of resource consumption and efficiency trends in Bangladesh, India and Pakistan: economy-wide biotic and abiotic material flow accounting from 1978 to 2017. Journal of Cleaner Production, 250, 119554. https://doi.org/10.1016/j.jclepro.2019.119554.
Shahbaz, M., Lahiani, A., Abosedra, S. & Hammoudeh, S. (2018). The role of globalization in energy consumption: a quantile cointegrating regression approach. Energy Economics. 71, 161–170.
Shao, Q., Schaffartzik, A., Mayer, A. & Krausmann, F. (2017). The high ‘price’ of dematerialization: a dynamic panel data analysis of material use and economic recession. Journal of Cleaner Production, 167, 120–132. https://doi.org/10.1016/j. jclepro.2017.08.158.
Shao, S. & Razzaq, A. (2022). Does composite fiscal decentralization reduce trade-adjusted resource consumption through institutional governance, human capital, and infrastructure development? Resources Policy, 79, 103034, https://doi.org/10.1016/j. resourpol.2022.103034.
Steckel, J.C., Brecha, R.J., Jakob, M., Strefler, J. & Luderer, G. (2013). Development without energy? Assessing future scenarios of energy consumption in developing countries. Ecological Economics, 90, 53–67.
Steinberger, J.K., Krausmann, F. & Eisenmenger, N. (2010). Global patterns of materials use: a socioeconomic and geophysical analysis. Ecological Economics, 69, 1148–1158. https:// doi.org/10.1016/j.ecolecon.2009.12.009.
Steinberger, J.K., Krausmann, F., Getzner, M., Schandl, H. & West, J. (2013). Development and dematerialization: an international study. PLOS One 8, e70385. https://doi.org/ 10.1371/journal.pone.0070385.
Telega, I. & Telega, A. (2019). Driving factors of material consumption in European countries – spatial panel data analysis. Journal of Environmental Economics and Policy, 1–12. https://doi. org/10.1080/21606544.2019.1675186.
Torras, M. & Boyce, J.K. (1998). Income, inequality, and pollution: a reassessment of the environmental Kuznets Curve. Ecological Economics, 25, 147–160. https://doi.org/10.1016/ S0921-8009(97)00177-8.
Ullah, S., & Lin, B. (2024). Harnessing the synergistic impacts of financial structure, industrialization, and ecological footprint through the lens of the EKC hypothesis. Insights from Pakistan. Energy, 132540.‌
Ulucak, R. & Bilgili, F. (2018). A reinvestigation of EKC model by ecological footprint measurement for high, middle and low income countries. Journal of Cleaner Production. https://doi. org/10.1016/j.jclepro.2018.03.191.
Ulucak, R., Koçak, E., Erdogan, S. & Kassouri, Y (2020). Investigating the non-linear effects of globalization on material consumption in the EU countries: Evidence from PSTR estimation. Resources Policy, 67, 101667.
Vehmas, J., Luukkanen, J. & Kaivo-oja, J. (2007). Linking analyses and environmental Kuznets curves for aggregated material flows in the EU. Journal of Cleaner Production, 15, 1662–1673. https://doi.org/10.1016/j.jclepro.2006.08.010.
Wang, H. & Wei, W. (2020). Coordinating technological progress and environmental regulation in CO2 mitigation: the optimal levels for OECD countries & emerging economies. Energy Economics, 87, 104510.
Wang, H., Zhao, X., Zhang, Z., Li, S. & Yuan, J. (2019). Material flows embodied in China–Japan trade. In: Advances in Intelligent Systems and Computing. Springer Verlag, pp. 338–344. https://doi.org/10.1007/978-3-030-15740-1_49.
Watari, T., Nansai, K. & Nakajima, K. (2021). Contraction and convergence of in-use metal stocks to meet climate goals. Global Environmental Change, 69,102284.
West, J. & Schandl, H. (2018). Explanatory variables for national socio-metabolic profiles and the question of forecasting national material flows in a globalized economy. Journal of Industrial Ecology. 22, 1451–1464. https://doi.org/10.1111/jiec.12671.
Wiedenhofer, D., Fishman, T., Lauk, C., Haas, W. & Krausmann, F. (2019). Integrating material stock dynamics into economy-wide material flow accounting: concepts, modelling, and global application for 1900–2050. Ecological Economics, 156, 121–133. https:// doi.org/10.1016/j.ecolecon.2018.09.010.
York, R., Rosa, E.A. & Dietz, T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46, 351–365. https://doi.org/10.1016/S0921-8009(03)00188-5.
Zhang, C., Chen, W.Q., Liu, G. & Zhu, D.J. (2017). Economic growth and the evolution of material cycles: an analytical framework integrating material flow and stock indicators. Ecol. Econ. 140, 265–274.