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Abstract China, being the largest global emitter of carbon, faces significant challenges in mitigating carbon emissions from energy consumption. The empirical findings reveal that the impact of energy consumption intensity and energy consumption structure on carbon emissions intensity is positively correlated with quantile levels and shows heterogeneous effects in different regions. Employing an energy consumption structure as a threshold variable to examine the impact of energy consumption intensity on carbon emissions intensity, we identify a significant single threshold effect in both the full sample and the western sample. Based on empirical research findings, this paper proposes specific policy recommendations.

Purpose This paper aims to examine the effect of insurance specific characteristics, corporate governance and risk reporting attributes, Shari’ah board and inflation rate on the financial performance of Takaful and cooperative insurance industries. Design/methodology/approach Based on a dynamic panel generalized method of moment’s system estimation, the author investigates determinants of financial performance as measured by the net premium written, earning ratio and profit margin. Findings Company size, insurance penetration, risk reporting and board size significantly explain the financial performance of both types of insurance companies. The effect of Shari’ah board and capital intensity on the financial performance of Takaful insurance is overall positive. The non-executive directors may negatively affect the financial performance. Additionally, positive relationship was also found between inflation rate and financial performance of cooperative insurance. Research limitations/implications The typical shortcomings of a content analysis-based research apply to the measurement of operational risk reporting variable. Some modifications need to be made if it were to be used for exploring the financial performance of other Islamic financial institutions. The structural model used in this paper can be used as a generic platform to develop a specific framework for other types of organizations. Practical implications Some suggestions may be functional for Islamic insurance regulatory authorities to intensify the transparency, and for insurers to channel an additional source of investment funding toward economic sectors. Originality/value The present study seeks to fill a demanding gap in the literature by providing new empirical evidence on the factors that influence the financial performance of the Islamic insurance sector. Moreover, the paper tries to distinguish and identify the determinants of the performance for Takaful and cooperative insurance companies operating in Saudi Arabia.

We consider the estimation of dynamic panel data models in the presence of incidental parameters in both dimensions: individual fixed-effects and time fixed-effects, as well as incidental parameters in the variances. We adopt the factor analytical approach by estimating the sample variance of individual effects rather than the effects themselves. In the presence of cross-sectional heteroskedasticity, the factor method estimates the average of the cross-sectional variances instead of the individual variances. The method thereby eliminates the incidental-parameter problem in the means and in the variances over the cross-sectional dimension. We further show that estimating the time effects and heteroskedasticities in the time dimension does not lead to the incidental-parameter bias even when T and N are comparable. Moreover, efficient and robust estimation is obtained by jointly estimating heteroskedasticities.

This paper provides estimation and inference methods for conditional average treatment effects (CATE) characterized by a high-dimensional parameter in both homogeneous cross-sectional and unit-heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable with explanatory variables. The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross-fitted residuals. This step uses a novel generic cross-fitting method that we design for weakly dependent time series and panel data. This method \"leaves out the neighbors\" when fitting nuisance components, and we theoretically power it by using Strassen's coupling. As a result, we can rely on any modern machine learning method in the first step, provided it learns the residuals well enough. Second, we construct an orthogonal (or residual) learner of CATE-the lasso CATE-that regresses the outcome residual on the vector of interactions of the residualized treatment with explanatory variables. If the complexity of CATE function is simpler than that of the first-stage regression, the orthogonal learner converges faster than the single-stage regression-based learner. Third, we perform simultaneous inference on parameters of the CATE function using debiasing. We also can use ordinary least squares in the last two steps when CATE is low-dimensional. In heterogeneous panel data settings, we model the unobserved unit heterogeneity as a weakly sparse deviation from Mundlak's (1978) model of correlated unit effects as a linear function of time-invariant covariates and make use of L1-penalization to estimate these models. We demonstrate our methods by estimating price elasticities of groceries based on scanner data. We note that our results are new even for the cross-sectional (i.i.d.) case.

The aim of this paper is to research, theoretically and empirically, the impact of institutional reforms on economic growth in transition countries (new European Union members) and Croatia, in the period from 1996 to 2012. In order to prove the hypothesis, we will use panel analysis of transition economies and Croatia, namely the Arellano-Bond dynamic panel analysis. The analysis includes two dependent variables (gross domestic product per capita [G.D.P./p.c.]and the share of export in G.D.P.) and five independent variables (total Heritage Index of Economic Freedom, Worldwide Governance Indicators (W.G.I.) government effectiveness indicator, W.G.I. rule of law indicator, corruption perception index and the index of institutional reforms in transition countries). The results show that there is a significant positive impact of institutional reforms on the economic growth of transition countries and Croatia, which creates preconditions that are essential for the future growth rate of the Croatian economy.

Do investors believe that firm-level (i.e., idiosyncratic) risk of green (i.e., environmentally responsible) firms is relatively lower? How does high market volatility affect the investors' view on the firm-level risk of green firms? This paper addresses these questions by investigating the relationship between firm-level (idiosyncratic) risk and firms' environmental performance. Further, we examine the effect market volatility has on the relationship. We estimate fixed-effect panel models using 8036 firm-year observations across 793 firms. We test robustness of the results with difference-in-difference (DiD), propensity score matching (PSM) and dynamic panel with the generalized method of moments (GMM) estimations. We find that investors generally associate firms that perform well on the environmental front to be of lower risk. However, during periods of high market volatility, just performing better than the industry does not make the investors see the firms' risk as being significantly lower. How well the firms perform in relation to the industry performance is associated with the investors believing that the firm's risk is significantly lower.

The benefits and costs of wildlife are contingent on the spatial overlap of animal populations with economic and recreational human activities. By using a production function approach with dynamic spatial panel data models, we analyze the effects of human hunting and carnivore predation pressure on the value of ungulate game harvests. The results show evidence of dynamic spatial dependence in the harvests of roe deer and wild boar, but not in those of moose, which is likely explained by the presence of harvesting quotas for the latter. Results suggest the impact of lynx on roe deer harvesting values is reduced by 75% when spatial effects are taken into account. The spatial analysis confirms that policymakers’ aim to reduce wild boar populations through increased hunting has been successful, an effect that was only visible when considering spatial effects.

This study aims to measure the degree of operational risk disclosure and examine its impact on operating cash flow of banks listed on the UAE Abu Dhabi Stock Exchange (ADX) and Dubai Financial Market (DFM) during the period 2003-2016. The authors conducted content analysis of the annual reports to measure the degree of operational risk disclosure. In addition, they used dynamic panel data regressions to analyze the impact of operational risk disclosure on the operating cash flow generated by the banks. The results show a low degree of operational risk disclosure for all UAE banks, both Islamic and conventional. In addition, the results show no association between the levels of disclosure of operational risk and cash flow for all banks, conventional and Islamic. Operational risk disclosure of Islamic banks has not been examined by any prior researchers. In addition, this paper examines the potential impact of operational risk disclosure on the operating cash flow generated by the banks.

Many companies compete globally in a world in which user ratings and price are important drivers of performance but whose importance may differ by country. This study builds on the cultural, economic, and structural differences across countries to examine how app popularity reacts to price and ratings, controlling for product characteristics. Estimated across 60 countries, a dynamic panel model with product-specific effects reveals that price sensitivity is higher in countries with higher masculinity and uncertainty avoidance. Ratings valence sensitivity is higher in countries with higher individualism and uncertainty avoidance, while ratings volume sensitivity is higher in countries with higher power distance and uncertainty avoidance and those that are richer and have more income equality. For managers, the authors visualize country groups and calculate how much price should decrease to compensate for a negative review or lack of reviews. For researchers, they highlight the moderators of the volume and valence effects of online ratings, which are becoming ubiquitous in this connected world.

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C-Lasso also achieves the oracle property so that group-specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C-Lasso is preserved in some special cases. Simulations demonstrate good finite-sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.