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China has witnessed low and stable consumer price inflation in conjunction with high and volatile food price inflation over the past decade. The purpose of this paper is to examine questions about whether or not the link between consumer price inflation and food price inflation has weakened and the determinants of consumer price inflation.

This paper explores these questions by estimating error correction terms for monetary and external sectors using the Johansen cointegration method.

Empirical results suggest that the link between consumer price inflation and food prices has not been weakened, food price inflation, especially cereal price inflation, remains a significant driving force for overall consumer price inflation, and international food prices also play a significant role in determining China's inflation dynamics.

The paper construct a multivariate dynamic model that features the link between consumer price inflation and its potential driving variables. It also develops error correction models for food price, non-food price and consumer price inflation, which can accommodate dynamic interactions among the underlying variables.

The modeling cost of the gradient-enhanced kriging (GEK) method is prohibitive for high-dimensional problems. This study aims to develop an efficient modeling strategy for the GEK method.

A two-step tuning strategy is proposed for the construction of the GEK model. First, an auxiliary kriging is built efficiently. Then, the hyperparameter of the kriging model is served as a good initial guess to that of the GEK model, and a local optimal search is further used to explore the search space of hyperparameter to guarantee the accuracy of the GEK model. In the construction of the auxiliary kriging, the maximal information coefficient is adopted to estimate the relative magnitude of the hyperparameter, which is used to transform the high-dimension maximum likelihood estimation problem into a one-dimensional optimization. The tuning problem of the auxiliary kriging becomes independent of the dimension. Therefore, the modeling efficiency can be improved significantly.

The performance of the proposed method is studied with analytic problems ranging from 10D to 50D and an 18D aerodynamic airfoil example. It is further compared with two efficient GEK modeling methods. The empirical experiments show that the proposed model can significantly improve the modeling efficiency without sacrificing accuracy compared with other efficient modeling methods.

This paper developed an efficient modeling strategy for GEK and demonstrated the effectiveness of the proposed method in modeling high-dimension problems.

The relationship between farm size and greenhouse gas (GHG) emissions has not been clearly defined. This paper aims to assess and compare the impact of farm size on greenhouse gas (GHG) emissions derived from wheat and maize production in the North China Plain (NCP), one of the most important agricultural regions in China.

A field survey through face-to-face interviews was conducted to collect the primary data, and life cycle assessment method, a worldwide comparable framework, was then adopted to characterize the farm-size effect on greenhouse gas (GHG) wheat and maize production in NCP.

It was confirmed that GHG emissions from N fertilizer production and use were the primary contributor to total carbon footprint (CF). As farm size increased, maize yield increased but wheat yield barely changed, while area-scaled and yield-scaled CF declined for both crops. These results were supposed to relate to utilize the inputs more efficiently resulting from increased application of modern agriculture methods on larger operations. It was also found maize not only had higher grain yields, but possessed much smaller CFs. More notably, the reduction of CF with farm size seemed to be more sensitive for maize as compared to wheat. To further mitigate GHG emissions, farm size should better be larger for wheat than for maize.

This study provides useful information guide for Chinese agriculture in increasing crop production, raising farm income and relieving environmental burdens caused by the misuse of agricultural resources.

The former residences of historical figures are typical landscape elements of historic districts, which are characterised by the styles of these residences and spiritual historical figures cultures. The purpose of this paper is to determine how the former residences respond once the historical figures living there have passed.

The history of human culture and progression of urban construction – which are submerged in societal transformation – is recorded for old Beijing city. Narrative space theory is used and methods such as a content analysis, map overlay and the Geographic Information System are employed to analyse the selected 300 former residences of historical figures in old Beijing city.

The results are as follows: the political setting played a key role in the evolution process, three political narrative areas in the inner city and one cultural narrative area in the outer city form the narrative spatial structure of the former residences of historical figures, “government construction” and “resident construction” are the main reasons for the loss and destruction of narrative spaces and ordinary life is an important channel for showcasing the history of former residences. The narrative spaces of these residences carry double histories, namely, the development of human history and of city construction.

Different from former studies that focus on the preservation of the single historical building, this study explores the integral logic of historic buildings in the whole city through narrative space theory to get a combination of culture and space.