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The purpose of this paper is to assess how spatial heterogeneity and production diversification have affected the efficiency of households participating in the SLCP.

Based on household survey data collected from three sample counties in the Loess Plateau region, the paper analyzes how spatial heterogeneity and business diversification have affected the production efficiency of households participating in the SLCP. Using four-step bootstrap, input stochastic distance frontiers of multi-inputs and multi-outputs are developed simultaneously with a technical efficiency effects model in which participating in the SLCP is treated as an exogenous variable.

The household production in the three counties is constrained by land, labor, capital, and fixed inputs; thus, it is germane to using a multi-input and multi-output household production technology to evaluate the SLCP. However, the relative importance of each input and output, the productive diversification, and the technological structure are differentiated in the three counties; estimating the regression with pooled data appears to conceal the influences of local factors and blur the specific divergence, compared to running county-based, separate regressions.

The effects of the SLCP on farm household production are location dependent. It is necessary to include spatial heterogeneity within public policy evaluation. In addition to suggesting localized and differentiated schemes of subsidy, this implies that the government may provide means to facilitate different regions to make the economic transition.

This paper intends to make two contributions. First, the analysis will capture and explain the adjustments induced by the SLCP in off-farm and other activities for entire household production, and special attention is given to the diversification of household production outputs. Second, this analysis sheds new light to the significance of spatial differentiation in mediating the effectiveness of a public policy or program.

This article investigates the mechanism of the direct and indirect effects of epidemics on agricultural production and projects the impact of COVID-19 on agricultural output in China.

This article first adopts a dynamic panel model and spatial Durbin model to estimate the direct and indirect effects, followed by a growth accounting method to identify the channels by which epidemics affect agriculture; finally, it projects the overall impact of COVID-19 on agriculture.

The incidence rate of epidemics in a province has a negative impact on that province's own agricultural productivity, but the increase in the input factors (land, fertilizer and machinery) can make up for the loss and thus lead to insignificant direct effects. However, this “input-offset-productivity” mechanism fails to radiate to the surrounding provinces and therefore leads to significant indirect/spillover effects. It is projected that COVID-19 will lower China's agricultural growth rate by 0.4%–2.0% in 2020 under different scenarios.

It is crucial to establish a timely disclosure and sharing system of epidemic information across provinces, improve the support and resilience of agricultural production in the short run and accelerate the process of agricultural modernization in the long run.

Considering the infectivity of epidemics, this article evaluates the mechanism of the direct and indirect effects by introducing a spatial dynamic model into the growth accounting framework. Moreover, besides the impact on input portfolio and productivity, this article also investigates whether epidemics reshape agricultural production processes due to panic effects and control measures.

One of the defining characteristics of industrial capitalism is the rapid expansion of social production. This expansion requires increased use of matter and energy. Society cannot create either matter or energy, so industrial expansion in one place means that matter and energy must be extracted and transported from other places. Because social production expands through both new technologies and new products, industrial growth requires not only greater amounts, but also an increasing variety of material and energetic forms. Because these different forms of matter and energy are found in limited quantities in different parts of the world, expansion, technological innovations, and product differentiation in productive economies entail the frequent relocation of extractive economies, either because they have depleted the natural resources on which they depend or because new technologies have shifted the market. Regions which depend on exporting extracted natural resources are therefore likely to suffer from severe fluctuations in income. Capital sunk in extractive infrastructure may devalue radically. These problems limit their capacity for sustained development. Nonetheless, resource extraction figures prominently in the economic plans of many lessdeveloped nations. A growing literature addresses the economic or the political pitfalls that beset extractive economies. This essay explores their ecological roots.

The purpose of this paper is to identify the effects of recent demographic transition and rising labor costs on agricultural production structure and pattern in China during 1998-2012.

The authors, first, theoretically discuss the effects of changing relative input prices due to rising labor cost on producers’ decisions regarding input mix (substitution effect), output level, and product quality (output effect). A logarithmic mean Divisia index decomposition method is then applied to empirically identify these effects at aggregated levels, followed by an analysis based on the visualization of land use indicators on changing cropping patterns across Chinese provinces.

The authors find that tightened effective agricultural labor supply and rises in rural labor costs are associated with divergent changes in input mixes and output choices across products. Producers of land-intensive products focusing more on input mix adjustment, while those of labor-intensive products seem to more likely to adjust output choices. Producers’ adaption strategies also varied across Chinese provinces due to natural conditions, leading to shifts and concentrations in the regional distribution of agricultural products, with lower-value bulk products concentrating in the plain areas, whereas higher-value horticulture products increasingly prevailing in sloped areas.

This paper illustrates how adjustments in input mixes and output choice in Chinese agriculture counteracted disadvantages caused by rising labor costs and how such adjustments are product and region specific. Based on these observations, implications regarding further innovations in production technology and institutional arrangements needed within China’s agricultural sector are highlighted in the paper.

Integrating smallholders into high‐value global markets represents a unique opportunity to effect large‐scale poverty reduction in the countryside. The purpose of the paper is to add empirical evidence to the discussion of how to best incorporate smallholders into the formal economy sustainably and responsibly.

The paper first builds a theoretical framework around global value chain theory and literature on smallholder inclusion and Kenya's growing horticultural sector. It then moves to explore a Kenyan smallholder‐based business model that incorporates 4,000 flower producers through an efficient and transparent intermediary. The analysis focuses on the importance of governance, upgrading and strong intermediaries for including smallholders in horticultural value chains.

In conclusion, this paper finds that although smallholder inclusion is both favorable and feasible based on theory, literature and case study analysis, it remains limited. It proposes embracing innovative smallholder‐based business models as a viable path out of poverty in countries with low labor costs, suitable climatic conditions and basic infrastructural capacities.

Limitations include a reliance on largely qualitative research methods due to gaps in available data.

Policy implications include the necessity of promoting agricultural development through investments in extension services, the creation of research and development centers and improvements in the rule of law.

This paper is unique in its focus on business models and global value chains as mechanisms through which to include smallholders into the global economy.

The purpose of this paper is to rethink the focus of the smart cities debate and to open it to policymaking and strategy considerations. To this end, the origins of what is termed normative bias in smart cities research are identified and a case made for a holistic, scalable and human-centred smart cities research agenda. Applicable across the micro, mezzo and macro levels of the context in which smart cities develop, this research agenda remains sensitive to the limitations and enablers inherent in these contexts. Policymaking and strategy consideration are incorporated in the agenda this paper advances, thus creating the prospect of bridging the normative and the empirical in smart cities research.

This paper queries the smart cities debate and, by reference to megacities research, argues that the smart city remains an overly normatively laden concept frequently discussed in separation from the broader socio-political and economic contexts in which it is embedded. By focusing on what is termed the normative bias of smart cities research, this paper introduces the nested clusters model. By advocating the inclusion of policymaking and strategy considerations in the smart cities debate, a case is made for a holistic, scalable and human-centred smart cities agenda focused, on the one hand, on individuals and citizens inhabiting smart cities and, on the other hand, on interdependencies that unfold between a given smart city and the context in which it is embedded.

This paper delineates the research focus and scope of the megacities and smart cities debates respectively. It locates the origins of normative bias inherent in smart cities research and, by making a case for holistic, scalable and human-centred smart cities research, suggests ways of bypassing that bias. It is argued that smart cities research has the potential of contributing to research on megacities (smart megacities and clusters), cities (smart cities) and villages (smart villages). The notions of policymaking and strategy, and ultimately of governance, are brought into the spotlight. Against this backdrop, it is argued that smart cities research needs to be based on real tangible experiences of individuals inhabiting rural and urban space and that it also needs to mirror and feed into policy-design and policymaking processes.

The paper stresses the need to explore the question of how the specific contexts in which cities/urban areas are located influence those cities/urban areas’ growth and development strategies. It also postulates new avenues of inter and multidisciplinary research geared toward building bridges between the normative and the empirical in the smart cities debate. More research is needed to advance these imperatives at the micro, mezzo and macro levels.

By highlighting the connection, relatively under-represented in the literature, between the normative and the empirical in smart cities research, this paper encourages a more structured debate between academia and policymakers focused on the sustainable development of cities/urban areas. In doing so, it also advocates policies and strategies conducive to strengthening individuals’/citizens’ ability to benefit from and contribute to smart cities development, thereby making them sustainable.

This paper makes a case for pragmatic and demand-driven smart cities research, i.e. based on the frequently very basic needs of individuals and citizens inhabiting not only urban but also rural areas. It highlights the role of basic infrastructure as the key enabler/inhibitor of information and communication technology-enhanced services. The nested clusters model introduced in this paper suggests that an intimate connection exists between individuals’ well-being, their active civic engagement and smart cities sustainability.

This paper delineates the relationship between megacities and smart cities research. It identifies the sources of what is termed normative bias in smart cities research. To address the implications of that bias, a nested clusters model for smart cities is introduced, i.e. a conceptual framework that allows us to redraw the debate on smart cities and establish a functional connection between the array of normatively laden ideas of what a smart city could be and what is feasible, and under which conditions at the policymaking level.

This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

This chapter examines the notion of spatial justice and its applicability in the context of informality. In the first part it examines the concept of informal urbanism in the context of informal settlements and experiences with upgrading programmes. Drawing on critical evaluations of some of the most well-known upgrading programmes, the chapter in the second part then theorises the notion of spatial justice. I argue for a multidimensional, fragmentary, context- and culture-specific concept, which encompasses equity, empowerment and recognition. As an operationalisation of the notion of spatial justice developed in this chapter and a conclusion to it, a framework is proposed to underpin the assessment of spatial justice in public space based upgrading programmes in informal settlements. It contains aspects regarding the quality of the material space, the integrity of its production process, issues of management and regulation as well as the use of space.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP) approach. The SIDSS was used to measure the productivity of rubber plantation and rubber agroindustry by GP approach, and select the best strategies for increasing the productivity of rubber agroindustry.

This system was developed by combining spatial analysis, GP, and fuzzy analytic network process (ANP) with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry. Rubber plantation productivity measurement model was used to find the productivity level of rubber plantation with fuzzy logic, and also to provide information and decision alternatives to all stakeholders regarding spatial condition of rubber agroindustry, production process flow, and analysis of the seven green wastes at each production process flow using the geographic information system. GP measurement model was used to determine the productivity performance of the rubber agroindustry with the green productivity index (GPI). The best strategy for increasing the productivity was determined with fuzzy ANP.

Rubber plantation measurement model showed that the average of plantation productivity was 6.25 kg/ha/day. GP measurement model showed that the GPI value of ribbed smoked sheet (RSS) was 0.730, whereas of crumb rubber (CR) was 0.126. The best strategy for increasing the productivity of rubber agroindustry was raw material characteristics control. Based on the best strategy, the GPI value of RSS was 1.340, whereas of CR was 0.228.

This decision support system is still limited as it is based on static data; it needs further development so that it can be more dynamically based on developments in the rubber agroindustry related levels of productivity and environmental impact. In addition, details regarding the decision to increase the productivity of the rubber section by benchmarking efforts should be studied further, both among plantation as well as among countries such as Thailand so that the productivity of rubber plantation and agroindustry can be integrated.

This research can help the planters to select superior clones for rubber trees, to improve the technique of tapping latex, and to use a better coagulant. The good quality and quantity of raw material is a key factor in increasing the productivity of rubber agroindustry; if the quality of latex is good then the resulting product will also have a good quality and production cost can be reduced. In addition, the application of GP through the calculation of GPI value using improvement scenarios can be used as a reference and comparison for evaluating the performance of rubber agroindustry to reduce the waste generated by the activities of rubber processing plant.

Reduction of waste generated by production activities can improve the quality of life of the workforce and the environment. The calculation of GPI value can also be used as a basis to use raw materials, water, and electricity more efficiently.

This system was developed by combining spatial analysis, GP, and fuzzy ANP with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry.