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The integrated reports published by companies vary significantly in quality in spite of them claiming to be compliant with the integrated reporting (IR) Framework issued by the International Integrated Reporting Council (IIRC). The purpose of this paper is to develop and apply a normative benchmark against which compliance with the IR Framework, and the extent to which integrated reports make visible how organisations create value, can be evaluated.

The three pillars of the IR Framework – Capitals, Content Elements and the Guiding Principles – are operationalised by the way of a set of disclosure items that capture the extent to which they manifest within integrated reports. The created disclosure index is applied to analyse reports of five companies that are expected to be superior integrated reporters.

The normative benchmark that was created to operationalise the IR Framework identifies a vast amount of potentially communicable information and various degrees to which information may be disclosed. The integrated reports analysed differ significantly in the extent to which value-creation stories are made visible, despite some of the companies promoting to have actively engaged with IR as participants of the IIRC Pilot Program Business Network. All selected companies performed poorly in comparison to the normative benchmark.

This paper is the first to provide a comprehensive normative benchmark for analysing and evaluating compliance with the IR Framework and the extent to which integrated reports make visible how organisations create value.

With the end of the Cultural Revolution and the rise of Deng Xiaoping, China began a new era of economic and political reform. In 1978 the open door policy was initiated. In October of 1984, Deng Xiaoping set in motion an ambitious program of financial and industrial reform aimed at eventually restructuring China's economy into a vaguely defined market system. This “second revolution” has run into difficulties because the Chinese officials lacked experience controlling a supply and demand economy and because of the opposition from conservative factions within the Chinese bureaucracy.

Currently, there is a dearth of research studies regarding macro analysis of the workforce productivity of the US construction industry. The purpose of this paper is to calculate the workforce productivity changes of the US construction industry from 2006 to 2016, with the number of laborers as input and value of construction industry as output.

The present study introduced the data envelopment analysis (DEA) based Malmquist productivity index model to measure the workforce productivity of the US construction industry from 2006 to 2016.

The results indicated that the workforce productivity of the US construction industry experienced a continuous decline, except for the increases from 2011 to 2013 and from 2014 to 2015. It was also shown that there were gaps in the workforce productivity development level among all states and nine regions in the US construction industry. Besides, the relationship between workforce productivity and four aspects, including real estate price, workforce, climate distribution and economic factors, was analyzed.

The calculation of the productivity of the US construction industry is based on the premise that the external environment is fixed and unchanged from 2006 to 2016, but the multi-level DEA model for further calculation is required for obtaining more effective conclusions.

This paper measures the workforce productivity of the US construction industry over the past 11 years, which added latest analysis and knowledge into the construction industry, providing decision-makers with advice and data support to formulate policies to improve workforce productivity.

This study provided both government decision-makers and industrial practitioners with important macro background environment information, which will facilitate the improvement of workforce productivity in the construction industry in different regions of the US.

According to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.

First, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.

The model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.

This paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.

The purpose of this paper is to examine the effect of adopting certified food production on chemical fertilizer and pesticide use in China.

The authors estimate fixed effect models to track the changes in agrochemical consumption at household level over time and evaluate the effect of certified food production, using an unbalanced panel data set covering 4,830 households in six provinces over the period 2005–2013.

On average, the authors do not find significant effects of certified food production on either chemical fertilizer or pesticide consumption among Chinese farmers. The effects are heterogeneous across villages, but the heterogeneous effects show no clear pattern that is consistent with different types of certification. The findings are robust to the use of alternative panel structure and certification indicators. The lack of knowledge about certification among farmers, the price premium and differences in regulation enforcement across regions may explain why the authors do not find negative effects on agrochemical use.

This study suggests that careful inspections and strong enforcement of certified food production is needed to ensure that the environmental goals of certified food production can be achieved and the reputation of certification in China can be improved. The inspection of certification producers and the enforcement of current regulations should be stricter for the further healthy development of certified food production in China.

This study is the first attempt to systematically evaluate the impact of food certification on the use of agrochemicals in Chinese agriculture.

Through empirical analysis of Sino-US solar photovoltaic (PV) trade, this paper aims to evaluate the complementarity of Sino-US solar PV trade by adopting trade combination degree (TCD) index, export similarity index (SI) and trade complementarity index (TCI). It also explores the role of trade disputes over Sino-US solar PV trade between China and the USA and important factors affecting the complementarity of the trade.

Based on the comparative advantage theory, this paper selects the TCD, export SI and TCI to evaluate the complementarity of Sino-US solar PV trade comprehensively. Among them, TCD and SI can directly reflect the degree of cooperation and competition of Sino-US solar PV trade. Finally, the authors further analyze the decisive factors affecting the complementarity of Sino-US PV trade by entropy weight method and multiple linear regression analysis on the influencing factors of TCI.

The solar PV trade between China and the USA still has a close relationship, and there is solar PV trade cooperation and competition between the two countries. The factors affecting the complementarity of Sino-US solar PV trade are mainly exchange rate levels rather than trade disputes between China and the USA. The solar PV trade policies of China and the USA will have a great negative impact on the global supply chain of solar PV products. The major solar PV products in China and the USA have a clear division in the global supply chain and still have a strong trade complementarity.

This paper conducts an empirical analysis of the Sino-US solar PV trade rather than a policy discussion. This research has important practical significance for the healthy and sustainable development of solar PV trade for both countries. It can also provide references to the current trade disputes between China and the USA in a broader sense.

The purpose of this paper is to explore the impact of using computers to obtain information on the farm household’s production and consumption based on a field survey of farm households in the northern China.

The most important methods applied are instrumental variable (IV) method and propensity score matching (PSM) method. Estimators of IV, PSM and nearest neighborhood matching approaches are considered together to check the robustness of empirical results.

This paper careful impact evaluation results suggest that the use of computer not only improves the size of arable land rented in but also reduces family labor input intensity and the probability of selling agricultural outputs at farm-gate markets. Moreover, it also stimulates transportation, garment, housing and insurance expenditures per capita.

The database of this research comprises cross-section data, which does not support a cross-time comparison.

These results imply that it is vital to expand the coverage of computer use in rural areas. This may suggest that the importance of improving computer access is crucial for stimulating rural consumption increase. Furthermore, the need for the expansion of internet network coverage in western areas is also of importance.

First, the authors directly estimate computer usage impacts on a broader range of production and consumption indicators by including land-relative investments, variable investments, labor input and household’s expenditure and provide rigorous impact evaluations on the impact of access to computer. Second, the authors use IV and PSM methods to correct self-selection bias, going beyond the single equation approach in other studies. This enables us to identify the causal relationship between computer usage and farmer’s production and consumption decisions.