Multi-sectoral analysis of smarter urban nitrogen metabolism: A case study of Suzhou, China

The urban socioeconomic metabolism of multiple flows of resources is highly complex. Our concern is that of moderating the environmental burden and intensity of this metabolism and elevating its “circularity” through technological interventions in the composition of urban infrastructure. The paper addresses present and possible future patterns of the nitrogen (N) metabolism of Suzhou, China, using a Multi-sectoral Systems Analysis (MSA) model of the water, energy, food, forestry, and waste management sectors of the city's infrastructure and economy. Two of the Triple Bottom Lines are employed in the assessment: those of the economic feasibility and the environmental benignity of the technological interventions. Environmental benignity is gauged by three Metabolic Performance Metrics (MPMs). 15 scenarios of these interventions are assessed, each being a different combination of introducing four particular promising technologies. The MPMs allow ranking of the scenarios according to, first, the ratio of incoming resource flows into the city and outgoing socio-economically beneficial products and, second, the rates at which “wastes” of no value are released to the atmosphere (as air pollutants), hydrosphere (water pollutants), and the lithosphere (solid wastes). The economic feasibility of the options can similarly be ranked according to the monetary values of resources (such as clean water or energy) that are saved by lowered urban consumption or that are recovered (such as biofuels or fertilizer) for beneficial re-circulation and re-use. All four candidate technological innovations are associated with the water and waste management sectors of urban infrastructure. We show that the trio of urine-separating technology, cultivation of algal biomass in wastewater treatment, and pyrolysis of animal manure can achieve an expected net annual economic benefit of 1.4B Yuan, together with the potential to recover some 31 Gg N yr−1. Notably, our computational assessment accounts for uncertainty, using Monte Carlo simulation and sensitivity testing. In particular, we examine how uncertainty may significantly undermine the conclusions we may draw about the promise of any given technological innovation vis à vis another alternative.