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Lacking systematic supply-use information of agricultural biomass and food products within China makes the existing provincial environmental pressure assessments (e.g., water consumption) either not detailed enough (e. g., by the input-output table-based approach) or not comprehensive enough (e.g., by the process-based approach). This study develops a symmetric inter-provincial multiregional input-output (MRIO) model that hybridizes the physical food and agricultural biomass system with the monetary supply chain of China. First, we construct the inter-provincial supply, use, and input-output tables in physical units of 84 agriculture, food and forestry products. These physical supply/use/MRIO tables systematically capture agrifood product flows, at an unprecedented level of product detail, along domestic supply chains within China. Then we integrate the physical MRIO table of agrifood products into the monetary all-sector MRIO table to construct a symmetric hybrid MRIO table of China. The application of our hybrid MRIO model to the case of provincial blue water footprint assessments reveals that the hybrid model enhances both the monetary MRIO table-based approach and the process-based approach from different aspects. For instance, the hybrid MRIO model can reduce the uncertainty of monetary MRIO modeling due to the aggregation of products with different environmental properties into homogeneous sectors. Lastly, uncertainty analysis is implemented to quantify the main sources of uncertainties, and understand the reliability of our new hybrid MRIO model for future sustainable development design.

The paper combines grid-level data of eight emission types –CO2, N20, CH4, NH3, NOX, PM10, PM2.5, and SO2 – with sub-national economic data to create a 1995–2015 balanced panel for NUTS 2 regions in EU countries. Regions on average show decoupling of emissions from output but most of the emission reductions are achieved before the 2008 financial crisis. Post 2008, very weak decoupling and even coupling can be observed. Using OECD’s Environmental Policy Stringency (EPS) Index as an intervention variable, an event study analysis shows that strong policies significantly reduce emissions, but there is considerable heterogeneity in the response by emission types and regional income levels.

The long-lived, culturally relevant, and unique buildings of Europe’s urban landscapes embody the values of the circular economy (CE) and sustainability. They are central to urban identities, generation after generation. Furthermore, adaptive reuse of cultural heritage buildings (ARCH) contributes to slowing down the extraction of natural resources, reducing energy for new buildings, and reducing construction and demolition waste and greenhouse gas emissions. ARCH’s inherent characteristics distinctly express the 2019 European Green Deal’s CE objectives and the 2020 building Renovation Wave Strategy. In this context, European city managers, heritage conservationists, and other stakeholders need a systematic method to characterize the investment opportunity for ARCH, considering CE. To fill this methodological gap, this study introduces a new composite indicator called the Circular City Adaptive Reuse of Cultural Heritage Index.&nbsp

The growth in e-commerce has led to increased pressure within the courier, express and parcel (CEP) sector to tackle the ‘last-mile’ issue and come up with solutions that not only satisfy the customers, but also other stakeholders such as city councils and other regulatory bodies. Scholars have highlighted micro-hubs and the associated horizontal collaboration as a possible solution, which might help alleviate problems associated with last-mile logistics in inner-city centers. However, trust and data exchange issues are considerable barriers to the introduction of horizontal collaboration, in particular between CEP carriers. To address the lack of trust and the issue of data exchange between carriers, the use of blockchain technology may present a solution, but existing research so far is limited concerning frameworks that specifically discuss blockchain technology in the context of micro-hubs and last-mile deliveries. In response, this paper presents a blockchain decision framework for a horizontal collaboration between CEP carriers based on key characteristics of existing blockchain decision models and relevant related research in the area of logistics and last-mile distribution. This is the first study that specifically addresses the use of blockchain technology for horizontal collaboration in the context of micro-hubs and last-mile deliveries.

Circular economy strategies seek to reduce the total resources extracted from the environment and reduce the wastes that human activities generate in pursuit of human wellbeing. Circular Economy concepts are well suited to the building and construction sector in cities. For example, refurbishing and adaptively reusing underutilized or abandoned buildings can revitalize neighborhoods whilst achieving environmental benefits. Cultural heritage buildings hold a unique niche in the urban landscape. In addition to shelter, they embody the local cultural and historic characteristics that define communities. Therefore, extending their useful lifespan has multiple benefits that extend beyond the project itself to the surrounding area, contributing to economic and social development. To explore this complex issue, the research applies systematic literature review and synthesis methods. Decision makers lack knowledge of the environmental benefits of adaptive reuse of cultural heritage buildings and lack tools to implement these projects. A new comprehensive circular economy framework for the adaptive reuse of cultural heritage buildings to reduce environmental impacts intends to meet these needs. The framework integrates methods and techniques from the building and construction literature that aim to reduce lifecycle environmental impact of buildings with a circular product supply chain approach.

This paper presents a post-Keynesian ecological macro model that combines three strands of literature: the directed technological change mechanism developed in mainstream endogenous growth theory models, the ecological economic literature which highlights the role of green innovation and material flows, and the post-Keynesian school which provides a framework to deal with the demand side of the economy, financial flows, and inter- and intra-sectoral behavioral interactions. The model is stock-flow consistent and introduces research and development (R&D) as a component of GDP funded by private firm investment and public expenditure. The economy uses three complimentary inputs - Labor, Capital, and (non-renewable) Resources. Input productivities depend on RD a Resource tax increase, and an increase in the share of public R&D on Resources. Model results show that policy instruments that are continually increased over a long-time horizon have better chances of achieving a "green" transition than one-of climate policy shocks to the system, that primarily have a short-run affect.

Using a consistent dataset for eighteen EU countries, six economic sectors, and six pollution indicators, we analyze decoupling of production–based emissions from GDP growth from 1995 to 2008. Computing decoupling factors as defined by the OECD (2002), we find that in almost all sectors and by almost all pollutants the median EU country had at least some decoupling. However, considerable heterogeneity in its magnitude can be observed across countries, sectors, and pollutants. For most pollutants and sectors, median decoupling performance improved from 2001–2008 compared to 1995–2001, while between–country disparities increased simultaneously. In a second step, we investigate country–level changes in decoupling states between the two sub–periods based on Tapio (2005). We find high diversity across countries and over time. To explain these differences across countries and sectors, we assess the impact of environmental policy stringency, and find tentative evidence that stricter policy encourages decoupling, however the effects are small and imprecise, differ by economic sector and pollutant, and take several years to materialize.

Understanding teleconnections of regional consumption patterns and global land use supports policy making towards achieving sustainable land use. We present an innovative globally consistent hybrid land-flow accounting method to track biomass flows and embodied land along global supply chains. It uses the large FAOSTAT database, which is, for non-food commodities, complemented with a multi-regional input-output model. We employ the hybrid model globally between 1995 and 2010 and present results for 21 regional markets. Results highlight the growing integration in international markets. In 2010, 31% of cropland cultivation was for export markets compared to 16% in 1995. The higher land demand of livestock-based diets, which account for one third of global cropland use, and differences in land use intensities cause large regional variations in extents and composition of land footprints. The utilization of cropland changed towards a growing importance of the non-food sector accounting for 12% in 2010. Comparing land quality weighted cropland footprints across regions further reveals large differences in the appropriation of available global cropland productivity. Because of large uncertainties and quality differences in the actual use of grassland for feeding ruminants, we propose land quality weighted grassland footprints to discuss the additional land use for ruminant livestock products.

Consumption-based material footprints calculated with multi-regional input–output (mrIO) analysis are influenced by the sectoral, spatial and material aggregations used in the mrIO tables, and lack of disaggregation can be a source of uncertainty. This study investigated the effect of the resolution of mrIO databases on consumption-based material footprints. The effect of aggregation was investigated by constructing input–output tables with different spatial, product and material category resolutions and comparing the calculated material footprints. Our results indicate that the material footprints of countries calculated using the different spatial and product aggregations are in general in the order of a few percent, with outliers in the order of 25% difference. The use of IO models with a low product category resolution (e.g. 60 product categories) to calculate the embodied material use of individual products will likely result in inaccurate estimations of the total embodied material for some product categories. Aggregating the original 46 material categories into 16 categories changes the calculated material footprint of countries by about 30%. This result strongly suggests that the material data used to create the extensions for the IO framework should be collected at the highest resolution that is practically feasible.

Adverse post-natural disaster outcomes in low-income regions, like elevated internal migration levels and low consumption levels, are the result of market failures, poor mechanisms for stabilizing income, and missing insurance markets, which force the affected population to respond, and adapt to the shock they face. In a spatial environment, with multiple locations with independent but inter-connected markets, these transitions quickly become complex and highly non-linear due to the feedback loops between the micro individual-level decisions and the meso location-wise market decisions. To capture these continuously evolving micro–meso interactions, this paper presents a spatially explicit bottom-up agent-based model to analyze natural disaster-like shocks to low-income regions. The aim of the model is to temporally and spatially track how population distributions, income, and consumption levels evolve, in order to identify low-income workers that are “food insecure”. The model is applied to the 2005 earthquake in northern Pakistan, which faced catastrophic losses and high levels of displacement in a short time span, and with market disruptions, resulted in high levels of food insecurity. The model is calibrated to pre-crisis trends, and shocked using distance-based output and labor loss functions to replicate the earthquake impact. Model results show, how various factors like existing income and saving levels, distance from the fault line, and connectivity to other locations, can give insights into the spatial and temporal emergence of vulnerabilities. The simulation framework presented here, leaps beyond existing modeling efforts, which usually deals with macro long-term loss estimates, and allows policy makers to come up with informed short-term policies in an environment where data is non-existent, policy response is time dependent, and resources are limited.

The paper describes our usage of and experience with the method of participatory systems mapping. The method, developed for the purpose of facilitating knowledge brokerage, builds on participatory modelling approaches and applications and was used in several events involving both researchers and policy makers. The paper presents and discusses examples of how different types of participatory interaction with causal loop diagrams ("system maps") produced different insights on issues related to sustainable consumption and enabled participatory reflection and sharing of knowledge. Together, these insights support a systemic understanding of the issues and thus the method provides instruments for coping with complexity when formulating policies for sustainable consumption. Furthermore the paper discusses the ability of the method - and its limits - to connect mental models of participants through structured discussion and thus bridge boundaries between different communities.

Debate regarding the relationship between socioeconomic development and natural disasters remains at the fore of global discussions, as the potential risk from climate extremes and uncertainty pose an increasing threat to developmental prospects. This study reviews statistical investigations of disaster and development linkages, across topics of macroeconomic growth, public governance and others to identify key challenges to the current approach to macro-level statistical investigation. Both theoretically and qualitatively, disaster is known to affect development through a number of channels: haphazard development, weak institutions, lack of social safety nets and short-termism of our decision-making practices are some of the factors that drive natural disaster risk. Developmental potentials, including the prospects for sustainable and equitable growth, are in turn threatened by such accumulation of disaster risks. However, quantitative evidence regarding these complex causality chains remains contested due to several reasons. A number of theoretical and methodological limitations have been identified, including the use of GDP as a proxy measurement of welfare, issues with natural disaster damage reporting and the adoption of ad hoc model specifications and variables, which render interpretation and cross-comparison of statistical analysis difficult. Additionally, while greater attention is paid to economic and institutional parameters such as GDP, remittance, corruption and public expenditure as opposed to hard-to-quantify yet critical factors such as environmental conditions and social vulnerabilities. These are gaps in our approach that hamper our comprehensive understanding of the disaster-development nexus. Important areas for further research are identified, including recognizing and addressing the data constraints, incorporating sustainability and equity concerns through alternatives to GDP, and finding novel approaches to examining the complex and dynamic relationships between risk, vulnerability, resilience, adaptive capacity and development.