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The purpose of this paper is to examine the relationships of technical training and the peer effects of technical training with farmers' pesticide use behaviors.

This study uses survey data from 300 peanut growers in Zoucheng County, Shandong, China, in 2016 and employs spatial econometric models to examine the relationships of technical training and the peer effects of technical training with farmers' pesticide use behaviors.

This paper reveals that important peer effects can be channeled through technical training and that these peer effects are sufficiently significant to encourage neighboring farmers to reduce the amount of pesticide use, to transform the structure of pesticide use, and to increase the usage amount of low-toxicity, low-residue pesticide use per hectare. The estimated parameters for the peer effects from technical training are significantly larger than those from technical training alone, which suggests that the technical training of neighboring farmers plays a greater role than technical training for farmers individually.

The research finds that technical training within smaller, localized, groups can induce previously unobservable spillover effects, and this provides a scientific, theoretical and empirical justification for agricultural technology extension that can lead to a rapid, effective transformation of applying new agricultural technologies in an environmentally sensitive and economically sustainable manner.

Stock markets are highly sensitive to foreign and domestic events. Stock exchange markets react promptly to news and are known as an indicator of good and bad trading conditions. Terrorist attacks leave adverse effects on the economy and cause stock price volatility and, consequently, stock return volatility. Therefore, this paper aims to analyze the spatial effects of terrorism on stock market returns in the Middle East from 2008 to 2019.

This paper uses analytical research design and estimates spatial model. Before estimating the spatial model, the spillover effects were confirmed for the spatial autoregressive model using Moran’s diagnostic test for spatial dependence, Geary’s C test and Akaike statistic.

The results of this study on spatial panel data and based on spatial autoregressive estimator indicated that terrorism and associated neighborhood effects had a negative impact on stock returns in Middle East countries. Also, the corruption index and oil price negatively affected stock market return in these countries, while the democracy index had a positive effect on stock market returns. According to the results, to achieve a high and stable stock market return, it is recommended that high-level consultation is pursued with leaders of involved countries to reduce the devastating effects of terrorist activities, increase political and economic stability, attract stockholders to stock markets and spend corresponding incomes developing the infrastructures in this sector in countries of this region.

Most of the studies have investigated the impact of terrorism on the stock market returns at the national or provincial level. However, the effect of terrorism on the stock market index in the tense region of the Middle East, which is the center of terrorist attacks in the world, has not been dealt with by considering the spatial econometric effects. Thus, to the best of the authors’ knowledge, this research is a first attempt to study the impact of terrorism on the stock market returns in Middle East countries using the spatial econometric approach.

A widely held belief before the 1990s – referred to as the oil-blessing hypothesis – was that oil discovery and production should promote economic growth and development and lead to poverty reduction. However, the so-called ‘oil-curse’ hypothesis, postulated by Sachs and Warner in 1995, challenged this belief, thus provoking a heated debate on the theme. The oil-curse hypothesis has been traditionally tested by means of cross-sectional and panel-data models. The author goes beyond these traditional methods to test whether the presence of spatial effects can alter the hypothesis in oil-producing African countries. In particular, this paper aims to investigate the effects on economic growth of oil production, oil resources and oil revenues along with the quality of democratic institutions, investment and openness to trade.

A Durbin spatial model, a cross-sectional model and panel-data model are used.

First, the validity of the spatial Durbin model is vindicated. Second, consistently with the oil-curse hypothesis, oil production, resources, rent and revenues have a negative and generally significant effect on economic growth. This result is robust for across the panel data, spatial Durbin and spatial autoregressive models and for different measures of spatial proximity between countries. Third, the author finds that the extent to which the business environment is perceived as benign for investment has a positive and marginally effect on economic growth. Additionally, economic growth of a country is further stimulated by a spatial proximity of a neighbouring country if the neighbouring country has created strong institutions protecting investments. Fourth, openness to international trade has a positive and marginally significant effect on economic growth.

This paper examines theories and studies that have been done before. However, as the related literature on the growth–resource abundance nexus has rarely examined spatial effects, this study seeks to test jointly the spatial effect and the neighbouring effect on the oil curse hypothesis.

The New Zealand (NZ) dairy industry faces the challenge of increasing productivity and dealing with public concerns over nutrient pollution. The effective policy needs to address regional differences in productivity and fertilizer use. The purpose of this paper is to investigate how spatial effects influence the relationship between dairy yields and intensive farming practices across regions in NZ.

This paper employs spatial panel data models to establish whether unobserved spatial effects exist in the relationship between dairy yields and nutrient inputs regionally and nationally using 2002, 2007 and 2012 data from Statistics NZ and DairyNZ.

The results show positive spatial spillovers for most intensive inputs. The high level of effluent use and estimated negative yield response to nitrogen suggests that an opportunity exists for greater use of effluent as a substitute for nitrogenous fertilizer. Substitution has the potential to reduce dependence on fertilizer and contribute to a reduction in the nutrient pollution.

This paper is the first empirical application of spatial econometric methods to examine the spatial relevance of dairy yields and intensive farming in NZ. In particular, the spatial panel data model accounts for cross-sectional dependence and controls for heterogeneity. The results contribute to an understanding of how farmers can improve their management of intensive inputs and contribute to the formation of regional environmental policy that recognizes regional heterogeneity.

The purpose of this study is to examine the spatial fluctuation spillover effect of green total factor productivity (GTFP) under the influence of Internet development.

Using panel data from 283 cities in China for the period 2003–2016, this paper explores the spatial fluctuation spillover effect of internet development on GTFP by applying the spatial autoregressive with autoregressive conditional heteroscedasticity model (SARspARCH).

The results of Moran's I test of the residual term and the Bayesian information criterion (BIC) value indicate that the GTFP has a spatial fluctuation spillover effect, and the estimated results of the SARspARCH model are more accurate than the spatial autoregressive (SAR) model and the spatial autoregressive conditional heteroscedasticity (spARCH) model. Specifically, the internet development had a positive spatial fluctuation spillover effect on GTFP in 2003, 2011, 2012 and 2014, and the volatility spillover effect weakens the positive spillover effect of internet development on GTFP. Moreover, Internet development has a significant positive spatial fluctuation spillover effect on GTFP averagely in eastern China and internet-based cities.

The results of this study provide digital solutions for policymakers in improving the level of GTFP in China, with more emphasis on regional synergistic governance to ensure growth.

This paper expands the research ideas for spatial econometric models and provides a more valuable reference for China to achieve green development.

Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals.

This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided.

Results show that the spatial autocorrelation problem is under control as shown by low Moran's I indexes. Moreover, this geo‐statistical approach provides coefficients on environmental amenities that are still highly significant by capturing only the remaining spatial autocorrelation.

The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.

The purpose of this study is to examine the net effect of golf courses’ proximity on home sale prices in Kern County, California.

A spatial Durbin error model is used with sales price data for 1,693 homes sold in Kern County in the third quarter of 2018. This paper compares 90 different spatial econometric models using Bayesian techniques to produce posterior model probabilities which guided model selection and the number of neighbors to use.

The results show that significant spatial dependence exists in home values in Kern County. Point estimates indicate that homes abutting golf courses are valued at less than those which are not. This study also finds that the farther away from golf courses the average home is, the higher its value.

This study contributes to the existing literature in three dimensions. First, this paper analyzes whether proximity to golf courses impacts home values in Kern County where a study of this nature has not been conducted. Second, the analysis uses transaction data for 2018 which was a period when the sport’s popularity was fading and golf courses closing. Third, Bayesian model comparison techniques are used to select the appropriate model.

This paper aims to consider spatial effects in the analysis of the relationship of revenue and service quality. When firms’ customers are located in spatially dispersed areas, it can be difficult to manage service quality on a geographically small scale because the relative importance of service quality might vary spatially. Moreover, standard approaches discussed so far in the marketing science literature usually neglect spatial effects, such as spatial dependencies (e.g. spatial autocorrelation) and spatial drift (spatial non-stationarity).

The authors propose a comprehensive but intelligible approach based on spatial econometric methods that cover spatial dependencies and spatial drift simultaneously. In particular, they incorporate the spatial expansion method (spatial drift) into spatial econometric models (e.g. spatial lag model).

Using real company data on seasonal ticket revenue (dependent variable) and service quality (independent variables) of a regional public transport service provider, the authors find that the elasticity for the length of the public transport network is between 0.2 and 0.5, whereas the elasticity for the headway is between −0.2 and 0.6, for example. The authors control for several socio-economic, socio-demographic and land-use variables.

Based on the empirical findings, the authors show that addressing spatial effects of service data can improve management’s ability to implement programs aimed at enhancing seasonal ticket revenue. Therefore, they derive a spatial revenue response function that enables managers to identify small-scale areas that are most efficient in terms of increasing revenue by service improvement.

The paper addresses the need to account for spatial effects in revenue response functions of public transport companies.

The purpose of this paper is to explore the sustainable development strategy of green finance under the background of big data.

From the perspective of big data, this paper uses quantitative and qualitative analysis methods to judge the correlation among green finance, environmental supervision and financial supervision. Green finance gives the entropy method to calculate the score of green finance and environmental regulation, and establishes the spatial lag model under the double fixed effects of time and space.

Spatial autocorrelation test shows that economic spatial weight matrix has obvious spatial effect on green innovation. Through the model selection test, the space lag model with fixed time and space is selected. The regression coefficients of green finance, environmental regulation and their interaction are 0.1598, 0.0541 and 0.1763, respectively, which significantly promote green innovation. The regression coefficients of openness, higher education level and per capita GDP are 0.0361, 0.0819 and 0.0686, respectively, which can significantly promote green innovation.

In view of the current situation of large-scale application of big data technology in green innovation of domestic energy-saving and environmental protection enterprises, this paper establishes a fixed time lag evaluation model of green innovation. M-test statistics show that the original hypothesis with no obvious spatial effect is rejected.

A burgeoning body of evidence points out the importance of spatial proximity in influencing firm efficiency besides internal characteristics. Nevertheless, the family status of the firm has been traditionally overlooked in that debate. Therefore, this study aims to investigate productivity spillovers stemming from the geographical closeness to innovators and family firms.

Using secondary data on Italian technology-intensive manufacturing firms, the paper exploits spatial econometric models to estimate productivity spillovers across firms.

As regards the presence of spatial dependence, this study reveals that a firm's level of efficiency and productivity is influenced by that of nearby firms. Specifically, three main results emerge. First, spatial proximity to innovators is beneficial for the productivity of neighbouring firms. Second, closeness to family firms is a source of negative externalities for spatially proximate firms. However, and this is the third result, the adverse effect vanishes when the nearby family firms are also innovators.

As the study relies on cross-sectional data, future research should explore productivity spillovers in a longitudinal setting. Additionally, the channels through which productivity spillovers occur should be measured.

The study highlights the importance of co-location for public policy initiatives to strengthen the competitiveness of firms and, indirectly, that of localities and regions. Moreover, the findings show the crucial role of innovation in mitigating the productivity gap between family and non-family firms.

Notwithstanding the advent of the digital era, spatial proximity and localized social relationships are still a relevant factor affecting firms' performance.

By exploring the role of family firms in influencing the advantages of geographical proximity, this study contributes to the growing efforts to explore family enterprises across spatial settings.