Regional consumption, material flows, and their driving forces: A case study of China's Beijing–Tianjin–Hebei (Jing–Jin–Ji) urban agglomeration

Abstract

Continuous urbanization and a coordinated regional development strategy have gradually shaped urban agglomerations as new and massive centers of resource consumption in China. Therefore, understanding the material consumption status and the underlying mechanisms for typical Chinese urban agglomerations will support efforts to promote regional resource‐utilization sustainability. In this study, we analyzed material consumption and its structure in the Beijing–Tianjin–Hebei (Jing–Jin–Ji) urban agglomeration from 2000 to 2015 and traced the sources of material consumption and underlying processes from a metabolic perspective. We also identified the main contributors and key drivers behind the changes of consumption during this period. The urban agglomeration's total consumption increased 2.2 times compared to the 2,000 level during the study period, with metallic minerals accounting for the largest proportion. Highly developed cities, including Beijing, Tianjin, Shijiazhuang, Tangshan, and Handan, consumed the largest amounts of materials and generated the most wastes. These cities also relied heavily on both internal and external resources and exchanged materials frequently among their metabolic compartments. Economic activity was the strongest contributor to the increased material consumption, followed by the population increase, whereas decreasing material‐consumption intensity (increased efficiency) restrained the growth of material consumption somewhat. Our application of the material‐flow accounting framework at the scale of an urban agglomeration provides support for future research on material consumption in other typical urban agglomerations, where it can provide support for policy development to alleviate regional resource shortages.

Drivers of urban metabolism: Toward a framework for urban transformations

“…In contrast, six studies recommended improvements in resource utilization efficiency, although they did not actually address efficiency in F I G U R E 3 Proposed framework to understand and operationalize the link between the drivers of metabolic flows and three common future visions: business as usual scenarios (BAU, top panel), transitions (middle panel), and radical visions of the future (transformation, bottom panel). their analysis (Athanassiadis et al, 2017;Kennedy et al, 2015;Li et al, 2021). In other words, studies show that economic growth correlates with a growing urban metabolism, but to contain this growing UM, they recommend increasing efficiency.…”

“…These studies seek to establish quantitative relationships between drivers and metabolic indicators (e.g. domestic material consumption [DMC], direct material input [DMI], or energy use), using regression analysis (Athanassiadis et al, 2017;Bettignies et al, 2019;Kalmykova et al, 2016;Kennedy et al, 2015) or decomposition analysis (Chen & Chen, 2017;Deng et al, 2022;Li et al, 2019Li et al, , 2021Sun et al, 2023;Zucaro et al, 2014). Studies using statistical methods rely on large datasets on both the metabolic indicators and the drivers, datasets that span multiple years and cover different scales, from a single city or a small group of (related) cities (Chen & Chen, 2017;Deng et al, 2022;Kalmykova et al, 2016;Li et al, 2019;Zucaro et al, 2014), to several cities around the globe (Iablonovski & Bognon, 2020;Kennedy et al, 2015), to intra-urban microscales (Athanassiadis et al, 2017;Bettignies et al, 2019;Porse et al, 2016).…”

“…Most of the 26 studies do not explicitly engage with questions concerning the future. Five of them do not address the future at all, and another 13 only do so indirectly by providing policy and research recommendations (Figure 2c), for example, policy incentives to reduce consumption (Bahers et al., 2020; Kalmykova et al., 2016), to increase the energy and resource efficiency of buildings (Athanassiadis et al., 2017; Kennedy et al., 2015; Li et al., 2021; Zucaro et al., 2014), or to pay closer attention to local contexts and resources (Bettignies et al., 2019; Zucaro et al., 2014). Three studies created specific future scenarios that were then evaluated and compared with the current situation (Chen & Chen, 2017; Peponi et al., 2022) and with each other (Marin & De Meulder, 2018).…”

“…Concerning the type of future vision represented in each of these studies, we distinguished three different attitudes or visions of what an imaginable or desirable future could be: a business‐as‐usual (BAU) vision, in which scenarios and recommendations refer to a future state of the system that is an extrapolation of the present (Li et al., 2019, 2021); a transition to a more sustainable future state. Here we refer to studies whose authors use terms like ecological or energy transition (Bahers et al., 2020; Bettignies et al., 2019; Dijst et al., 2018; Iablonovski & Bognon, 2020), and to studies that refer to more sustainable future states (Athanassiadis et al., 2017; Bettignies et al., 2019; Voskamp et al., 2020); a transformational vision of a radically different future.…”

“…Abbreviations: BAU, business as usual; CDD, cooling demand days; DMC, domestic material consumption; DMI, direct material input; HDD, heating demand days; HDI, human development index.1(Voskamp et al, 2020), 2(Iablonovski & Bognon, 2020), 3(Li et al, 2021), 4 (Li et al, 2019, 5(Bettignies et al, 2019), 6 (Chen & Chen, 2017), 7(Athanassiadis et al, 2017), 8(Wolfram et al, 2016), 9(Porse et al, 2016), 10 (Kalmykova et al, 2016, 11(Kennedy et al, 2015), 12(Zucaro et al, 2014), 13(Broto et al, 2012), 14(Pincetl, 2012), 15(Newell & Goldstein, 2019), 16(Peponi et al, 2022), 17(Marin & De Meulder, 2018), 18 (Goldstein et al 2013, 19(Pincetl et al, 2012), 20(Pichler et al, 2017), 21(Dijst et al, 2018), 22(Bahers et al, 2020), 23(Corvellec et al, 2013), 24(Liu et al, 2005), 25(Deng et al, 2022), 26…”