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This book is about development and measuring development progress. While precise definitions may vary, development is, at heart, a process of building wealth, the produced, natural, human, and institutional capital which is the source of income and wellbeing. A key finding is that it is intangible wealth, human and institutional capital, which dominates the wealth of all countries, rising as a share of the total as countries climb the development ladder. The book is divided into two parts. The first part provides the big picture of changes in wealth by income group and geographic region, with a focus on natural capital because it is especially important for low-income developing countries. The second part presents case studies that illustrate particular aspects of wealth accounting, including accounting for climate change, the role of intangible capital in growth and development, measuring human capital, and the use of wealth accounting to improve transparency and governance in resource-rich economies. The final chapter reports on the implementation of wealth accounting by countries. The appendixes provide the full wealth accounts for individual countries and for aggregations by income group and geographic region.

For a smart city, soft or non-physical assets share an important capital component with many impacts in different contexts. They enable a city to deliver and mainstream a people-centered policy in addition to the benefits provided by traditional, hard infrastructure. Soft assets can involve social and human capital, knowledge, participation, and innovative approaches that drive value in the city. However, it is always a challenge for city policy makers to identify and strengthen these soft assets using a systemic approach due to their inherent characteristics. This paper argues that soft assets should be strategically integrated into the development process of smart and resilient cities. Therefore, exploring various approaches to prioritize soft asset consideration would provide helpful guidelines to city policy makers for municipal value creation, and identify where the greatest needs for soft or intangible assets lie. This paper examines how to identify and decide which soft assets should take priority in smart and resilient cities. The findings can assist policy makers in their consideration of an optimal mix and balance of soft assets required in the city to improve living structures for a people-centered approach.

The eastern North Carolina Coastal Area Management Act region is one of the most hurricane-prone areas of the United States. Hurricanes incur substantial damage and economic losses because structures located near the coast tend to be high value as well as particularly exposed. To bolster disaster mitigation and community resilience, it is crucial to understand how hurricane hazards drive social and economic impacts. We integrate detailed hazard simulations, property data, and labor compensation estimates to comprehensively analyze hurricanes’ economic impacts. This study investigates the spatial distribution of probabilistic hurricane hazards, and concomitant property losses and labor impacts, pinpointing particularly hard hit areas. Relationships between capital and labor losses, social vulnerability, and asset values reveal the latter as the primary determinant of overall economic consequences.

Machine learning technology is widely used in the field of enterprise intangible value assessment due to its advantages in processing complex data and discovering linear relationships. This paper designs a B-P neural network model based on machine learning technology and compares it with the cost method, market method, income method, and option pricing B-S model for enterprise intangible asset value assessment. The performance of this paper’s model for predicting intangible assets is evaluated through enterprise transaction data collection and processing. In the training iteration of 40–80 rounds, this paper’s model loss, RMSE, accuracy, and recall successively converge to 0.08, 0.17, 0.91, and 0.96, and the relative error for the prediction of the value of enterprise intangible assets is low, which has a high performance in intangible value assessment. Additionally, this paper’s model computes the intangible asset value weights for various enterprises, and the results of expert judgments are essentially consistent. For instance, when analyzing human capital experts, the model calculates weights of 27.93% and 29.43%, respectively. This paper provides a scientific and accurate machine learning technology for enterprise intangible asset value assessment.

This paper examines how the reporting model for a firm's operating assets affects analyst forecast accuracy. We contrast U.K. and U.S. investment property firms having real estate as their primary operating asset, exploiting that U.K. (U.S.) firms report these assets at fair value (historical cost). We assess the accuracy of a balance-sheetbased forecast (net asset value, or NAV) and an income-statement-based forecast (earnings per share, or EPS). We predict and find higher NAV forecast accuracy for U.K. relative to U.S. firms, consistent with the fair value reporting model revealing private information that is incorporated into analysts' balance sheet forecasts. We find this difference is attenuated when the fair value and historical cost models are more likely to converge: during recessionary periods. Finally, we predict and find lower EPS forecast accuracy for U.K. firms when reporting under the full fair value model of IFRS, in which unrealized fair value gains and losses are included in net income. This is consistent with the full fair value model increasing the difficulty of forecasting net income through the inclusion of non-serially correlated elements such as these gains/losses. Information content analyses provide further support for these inferences. Overall, the results indicate that the fair value reporting model enhances analysts' ability to forecast the balance sheet, but the full fair value model reduces their ability to forecast net income.

PurposeThis paper investigates the performance of locally focused equity mutual funds (LFEFs) in Saudi Arabia as compared with the performance of benchmark funds. More specifically, the focal question pertains to whether Shariah-compliant mutual funds (SMFs) and conventional mutual funds (CMFs) outperform their respective benchmarks. Undertaken in the context of Saudi Arabia's economic planning under Vision 2030, the study offers a foundation for determining whether and the extent to which Shariah-compliant investment strategies are competitive—a matter of considerable importance across 57 Muslim countries.Design/methodology/approachThe Carhart four-factor model is applied to a sample of 39 Saudi Arabian mutual funds (MFs) using the monthly net asset value (NAV) per share. The sample period, April 2007 to October 2016, is considered in its entirety and as three sub-periods, i.e. low-, medium- and high-volatility.FindingsThe results show that the locally focused equity mutual funds (LFEFs) significantly outperformed their benchmark, i.e. the Tadawul All Share Index (TASI), during the full sample period and the low-volatility period. According to the empirical comparison, the CMFs also outperformed their TASI benchmark for the full sample period and the low-volatility period. However, the SMFs neither outperformed nor underperformed their S&P Saudi Arabia Domestic Shariah Index benchmark. That is, for each of the SMFs included in the sample, the Jensen's alpha was insignificant for both the full sample and all three volatility sub-periods.Research limitations/implicationsIn this paper, the four-factor model is used in the context of a single country. The results, therefore, may not be generalizable to the multi-country level in the Gulf Council Cooperation (GCC) region given differences between the member countries in terms of financial structure and economic focus.Practical implicationsThe results reported constitute a useful guide for policymakers and faith-based-sensitive investors concerned about the Shariah compliancy of their portfolios given that there is very little difference between how CMFs and SMFs performed in the focal period. This research can be extended to include other Islamic countries in the GCC region as a basis for identifying optimal investment vehicles, i.e. those most likely to produce high returns at low risk.Originality/valueThe work reported in this paper is original and constitutes a valuable asset for ethnoreligious-sensitive investors. The research has not been published in any capacity and is not under consideration for publication elsewhere.

The article presents a comprehensive asset management method. Here presented method aims to bridge the economic approach to asset management with the technical approach to road infrastructure life cycle, namely its resilience and performance. The presented asset value calculation methods are based both on socio-economic viewpoints on community benefits of an asset, as well as the technical aspect of the technical condition and residual life calculations of a road infrastructure. In contrast to common road asset management methods, asset value is not arbitrarily annually depreciated, instead, it is exactly calculated based on pavement performance models, pavement construction fatigue and paving material properties. Road asset value calculation is based on the asset performance and the technical condition of a pavement structure and other objects. Road asset performance is defined in terms of society and road user demands put on road category and its qualitative standard. Road asset technical condition is evaluated by the procurement cost calculation and condition deterioration. Value of condition deterioration is defined by residual life expectancy based on fatigue and construction reliability of the road infrastructure. The cross-asset allocation method is used for the creation of programs for claim and allocation of funding. The aim was to increase the credibility of the road administrators with the public as they present their decisions based on road asset management, and to increase the level of acceptance for practitioners.

With the rapid development and widespread application of the Internet of things (IoT), how to comprehensively and effectively evaluate the risk of the host in the IoT is of great significance. In the existing methods of evaluating IoT hosts based on attack graphs, the calculation of atomic attack probability and the asset value is unreasonable, and the impact of the association relationship between hosts on the risk value of the host is not considered. Aiming at the above problems, an IoT host risk assessment method based on an attack graph is proposed. First, the host-based attack graph is established according to the topology of the IoT, and then the vulnerability atomic attack probability and path attack probability are quantified according to multiple attribute values. After that, the improved weighted betweenness index is calculated from the perspective of the topology of the host-based attack graph. Furthermore, the asset value index weight is calculated by using the intercriteria correlation (CRITIC) method, and the host asset value is calculated according to the expert scoring results. Finally, the host risk is calculated according to the host attack probability, vulnerability impact value, the host improved weighted betweenness index, and the host asset value. The experimental results show that this method can evaluate the host risk in the IoT environment from a more comprehensive and reasonable point of view. The standard deviation of the host risk value is 0.09, which is increased by 25%, 13.9%, and 16.9% respectively compared with the asset correlation graph method, markov attack graph method and adjacency matrix method. This method facilitates the differentiation of the host's subsequent risk disposal priority.